<?xml version="1.0" encoding="UTF-8"?>
<xml><records><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Krzysztof Grochla</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Adam Józefiok</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tomasz Nycz</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ferhan Pekergin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ricardo Lent</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Georgia Sakellari</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Performance Evaluation of a Multiuser Interactive Networking System: A Comparison of Modelling Methods</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2012</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Computer and Information Sciences II</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><PAGES><style face="normal" font="default" size="100%">215–222</style></PAGES><ABSTRACT><style face="normal" font="default" size="100%">This article presents a queueing model for performance evaluation of a large database system at an assurance company. The system includes a server with a database and a local area network with a number of terminals where the company employees run applications that introduce documents or retrieve them from the database. We discuss a model of clients activities. Measurements were collected inside the working system: the phases at each users application performance were identified and their duration was measured. The collected data are used to construct a synthetic model of applications activities which is then applied to predict the system behaviour in case of the growth of the number of users. We apply simulation, Markov and diffusion models-their comparison, based on real data, may better verify the utility of particular methods than usual academic examples.</style></ABSTRACT><title><style face="normal" font="default" size="100%">Performance Evaluation of a Multiuser Interactive Networking System: A Comparison of Modelling Methods</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">T. Atmaca</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Krzysztof Grochla</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">T. Nycz</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ferhan Pekergin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">R. Lent</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">A model of boot-up storm dynamics</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2014</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Computer and Information Sciences III</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">London</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><PAGES><style face="normal" font="default" size="100%">371–379</style></PAGES><DATE><style face="normal" font="default" size="100%">10</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">The simultaneous boot of multiple virtual machines or networking devices imposes heavy load on the networking infrastructure. In large network of many virtual machines the coordination of the reboots and device registration is required. In the paper we present a simple analytical model computing the distribution of boot-up time, which is verified by comparison to a simulation model and provide analysis of the influence of several parameters on the overall boot-up time during a boot storm.</style></ABSTRACT><NOTES><style face="normal" font="default" size="100%">27th International Symposium of Computer and Information Sciences, Brenna,Poland, October 22-15, 2013</style></NOTES><title><style face="normal" font="default" size="100%">A model of boot-up storm dynamics</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">R. Lent</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Quantum Queuing Networks Throughput Optimisation</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2014</style></YEAR><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">ISCIS 29th Annual Symposium</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">X</style></DATE><ISBN><style face="normal" font="default" size="100%">978-3-319-09464-9</style></ISBN><ABSTRACT><style face="normal" font="default" size="100%">We study models of quantum queues based on discrete time quantum walks with barriers. Our considerations refer to multi-servers queuing models. Input and output of jobs in the queue are realized by systems consisting of quantum coins and walkers. We show that presented models behave differently from the classical ones. We also present numerical methods for optimisation of jobs processed by the system. We simultaneously maximise jobs throughput in the system and minimise the number of jobs lost.</style></ABSTRACT><title><style face="normal" font="default" size="100%">Quantum Queuing Networks Throughput Optimisation</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>7</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Monika Nycz</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tomasz Nycz</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Omer Abdelrahman</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Gokce Gorbil</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ricardo Lent</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Modelling Dynamics of TCP Flows in Very Large Network Topologies</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2016</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Information Sciences and Systems 2015: 30th International Symposium on Computer and Information Sciences (ISCIS 2015)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Cham</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer International Publishing</style></PUBLISHER><PAGES><style face="normal" font="default" size="100%">251–259</style></PAGES><ISBN><style face="normal" font="default" size="100%">978-3-319-22635-4</style></ISBN><URL><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-22635-4_23</style></URL><title><style face="normal" font="default" size="100%">Modelling Dynamics of TCP Flows in Very Large Network Topologies</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>7</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Sławomir Nowak</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mateusz Nowak</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Krzysztof Grochla</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piotr Pecka</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Krzysztof Grochla</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ricardo Lent</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Global Queue Pruning Method for Efficient Broadcast in Multihop Wireless Networks</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2016</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Computer and Information Sciences: 31st International Symposium, ISCIS 2016, Kraków, Poland, October 27–28, 2016, Proceedings</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Cham</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer International Publishing</style></PUBLISHER><PAGES><style face="normal" font="default" size="100%">214–224</style></PAGES><ISBN><style face="normal" font="default" size="100%">978-3-319-47217-1</style></ISBN><URL><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-47217-1_23</style></URL><title><style face="normal" font="default" size="100%">Global Queue Pruning Method for Efficient Broadcast in Multihop Wireless Networks</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>9</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Krzysztof Grochla</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ricardo Lent</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Computer and Information Sciences ISCIS 2016</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2016</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Communications in computer Information Science</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">659</style></VOLUME><NUMBER><style face="normal" font="default" size="100%">659</style></NUMBER><PAGES><style face="normal" font="default" size="100%">500</style></PAGES><EDITION><style face="normal" font="default" size="100%">Springer</style></EDITION><title><style face="normal" font="default" size="100%">Computer and Information Sciences ISCIS 2016</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">E.T Ceran</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Energy Packet Networks With Energy Harvesting</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2016</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE ACCESS</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">4</style></VOLUME><PAGES><style face="normal" font="default" size="100%">1321-1331</style></PAGES><DATE><style face="normal" font="default" size="100%">03/2016</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;We investigate the cooperation among energy prosumers (unified energy provider and consumer) through the energy packet network (EPN) paradigm, which represents both the flow of work that requires energy, and the flow of energy itself, in terms of discrete units. This paper details a stochastic model of EPNs, which is inspired from a branch of queuing theory called G-networks. The model allows us to compute the equilibrium state of a system that includes energy storage units, energy transmission networks, and energy consumers, together with the intermittent energy sources. The model is then used to show how the flow of work and energy in the system can be optimized for certain utility functions that consider both the needs of the consumers, and the desire to maintain some reserve energy for potential future needs.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Energy Packet Networks With Energy Harvesting</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">O Brun</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">L Wang</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Big Data for Autonomic Intercontinental Overlays</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2016</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE Journal on Selected Areas in Communications</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">34</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">3</style></ISSUE><PAGES><style face="normal" font="default" size="100%">575-583</style></PAGES><DATE><style face="normal" font="default" size="100%">02/2016</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;This paper uses big data and machine learning for the real-time management of Internet scale quality-of-service (QoS) route optimisation with an overlay network. Based on the collection of data sampled every 2 min over a large number of source-destinations pairs, we show that intercontinental Internet protocol (IP) paths are far from optimal with respect to QoS metrics such as end-to-end round-trip delay. We, therefore, develop a machine learning-based scheme that exploits large scale data collected from communicating node pairs in a multihop overlay network that uses IP between the overlay nodes, and selects paths that provide substantially better QoS than IP. Inspired from cognitive packet network protocol, it uses random neural networks with reinforcement learning based on the massive data that is collected, to select intermediate overlay hops. The routing scheme is illustrated on a 20-node intercontinental overlay network that collects some 2 × 106 measurements per week, and makes scalable distributed routing decisions. Experimental results show that this approach improves QoS significantly and efficiently&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Big Data for Autonomic Intercontinental Overlays</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">L. Wang</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Real-Time Traffic over the Cognitive Packet Network</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2016</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">23rd International Conference on Computer Networks (CN)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Brunów, Poland</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><VOLUME><style face="normal" font="default" size="100%">608</style></VOLUME><PAGES><style face="normal" font="default" size="100%">3-21</style></PAGES><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Real-Time services over IP (RTIP) have been increasingly significant due to the convergence of data networks worldwide around the IP standard, and the popularisation of the Internet. Real-Time applications have strict Quality of Service (QoS) constraint, which poses a major challenge to IP networks. The Cognitive Packet Network (CPN) has been designed as a QoS-driven protocol that addresses user-oriented QoS demands by adaptively routing packets based on online sensing and measurement, and in this paper we design and experimentally evaluate the“Real-Time (RT) over CPN” protocol which uses QoS goals that match the needs of real-time packet delivery in the presence of other background traffic under varied traffic conditions. The resulting design is evaluated via measurements of packet delay, delay variation (jitter) and packet loss ratio.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Real-Time Traffic over the Cognitive Packet Network</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">F. Francois</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Towards a cognitive routing engine for software defined networks</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2016</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE International Conference on Communications (ICC)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Kuala Lumpur, Malaysia</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><PAGES><style face="normal" font="default" size="100%">1-6</style></PAGES><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;In Software Defined Networks (SDN), intensive traffic monitoring is used to optimize the Quality-of-Service (QoS) of the network paths which are selected. Thus, we introduce the use of the Cognitive Packet Network (CPN) algorithm to SDN in order to optimize the search for new high-QoS paths. We install the CPN algorithm in the Cognitive Routing Engine (CRE), a new application software for SDN, and show that with limited monitoring overhead we are able to determine the near-optimal paths for given QoS metrics that may be proposed by the end users. Measurements that we have conducted on an experimental replica of the GEANT network that our approach uses close to 10 times less monitoring data than conventional SDN, but that we are able to approach the optimal paths within 2%.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Towards a cognitive routing engine for software defined networks</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Omer Abdelrahman</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Gokce Gorbil</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Detection and mitigation of signaling storms in mobile networks</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2016</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">International Conference on Computing, Networking and Communications (ICNC)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Kauai, Hawai, USA</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><PAGES><style face="normal" font="default" size="100%">1-5</style></PAGES><ISBN><style face="normal" font="default" size="100%">978-1-4673-8579-4</style></ISBN><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Mobile Networks are subject to “signaling storms” launched by malware or apps, which overload the the bandwidth at the cell, the backbone signaling servers, and Cloud servers, and may also deplete the battery power of mobile devices. This paper&lt;br /&gt;&#13;
reviews the subject and discusses a novel technique to detect and mitigate such signaling storms. Through a mathematical analysis we introduce a technique based on tracking time-out transitions in the signaling system that can substantially reduce both the number of misbehaving mobiles and the signaling overload in the backbone.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Detection and mitigation of signaling storms in mobile networks</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">W. Serrano</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">An Intelligent Internet Search Assistant Based on the Random Neural Network</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2016</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">12th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Thessaloniki, Greece</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><VOLUME><style face="normal" font="default" size="100%">475</style></VOLUME><PAGES><style face="normal" font="default" size="100%">141-153</style></PAGES><ISBN><style face="normal" font="default" size="100%">78-3-319-44943-2</style></ISBN><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Even web services that are free of charge, typically offer access to online information based on some form of economic interest of the web service itself. Thus advertisers who put the information on the web will make a payment to the search services based on the clicks that their advertisements receive. Thus end users cannot know that the results they obtain from Web search engines are exhaustive, or that they actually respond to their needs. To fill the gap between user needs and the information that is presented to them on the web, Intelligent Search Assistants have been proposed to act at the interface between users and search engines to present data to users in a manner that reflects their actual needs or their observed or stated preferences. This paper presents an Intelligent Internet Search Assistant based on the Random Neural Network that tracks the user’s preferences and makes a selection on the output of one or more search engines using the preferences that it has learned. We also introduce a “relevance metric” to compare the performance of our Intelligent Internet Search Assistant against a few search engines, showing that it provides better performance.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">An Intelligent Internet Search Assistant Based on the Random Neural Network</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Omer Abdelrahman</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">A Diffusion Model for Energy Harvesting Sensor Nodes</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2016</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">24th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Imperial Coll Campus, London, England</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><PAGES><style face="normal" font="default" size="100%">154-158</style></PAGES><ISBN><style face="normal" font="default" size="100%">978-1-5090-3432-1</style></ISBN><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Energy harvesting has recently attracted much interest due to the emergence of the Internet of Things, and the increasing need to operate wireless sensing devices in challenging environments without much human intervention and maintenance. This paper presents a novel approach for modeling the performance of an energy harvesting wireless sensor node, which takes into account fluctuations in the amount of energy extracted from the environment, energy loss due to battery leakage, as well as the energy cost of sensing, data processing and communication. The proposed approach departs from the common queueing-theoretic framework used in the literature, and instead uses Brownian motion to represent more accurately the time evolution of the distribution of the node's battery level. The paper derives some performance measures of interest along with the stationary solution of the system, and discusses possible directions for reducing the number of parameters and states of the model without compromising accuracy.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">A Diffusion Model for Energy Harvesting Sensor Nodes</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">F. Francois</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Optimizing Secure SDN-Enabled Inter-Data Centre Overlay Networks through Cognitive Routing</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2016</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">2016 IEEE 24th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Imperial Coll Campus, London, England</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><PAGES><style face="normal" font="default" size="100%">283-288</style></PAGES><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;More and more businesses are deploying their application(s) with different cloud providers which are close to their customers in order to provide better Quality of Service (QoS) to their end customers. In this work, an optimized and secure software-defined overlay network is proposed as an efficient mechanism to interconnect these geographically-dispersed applications compared to using only plain IP routing. A logically centralized Cognitive Routing Engine (CRE), based on Random Neural Networks with Reinforcement Learning, was developed to find with minimal monitoring overhead the optimal overlay paths when the public Internet is used as the communication means between the overlay nodes. CRE was evaluated by using an overlay network composed of hosts from 5 different public clouds where it was shown that the latency of CRE paths is most of the time within 5% of the latency of the optimal IP paths. Furthermore, it was also demonstrated that CRE is able to do asymmetric path optimization where the forward path is different from the reverse path for a given data centre pair in order to further improve QoS.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Optimizing Secure SDN-Enabled Inter-Data Centre Overlay Networks through Cognitive Routing</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Y Yin</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Deep learning with random neural networks</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2016</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">International Joint Conference on Neural Networks (IJCNN)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Vancouver, Canada</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><PAGES><style face="normal" font="default" size="100%">1633-1638</style></PAGES><DATE><style face="normal" font="default" size="100%">06/2016</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;This paper introduces techniques for Deep Learning in conjunction with spiked random neural networks that closely resemble the stochastic behaviour of biological neurons in mammalian brains. The paper introduces clusters of such random neural networks and obtains the characteristics of their collective behaviour. Combining this model with previous work on extreme learning machines, we develop multilayer architectures which structure Deep Learning Architectures a a “front end” of one or two layers of random neural networks, followed by an extreme learning machine. The approach is evaluated on a standard - and large - visual character recognition database, showing that the proposed approach can attain and exceed the performance of techniques that were previously reported in the literature.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Deep learning with random neural networks</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">L Wang</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">O Brun</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Adaptive workload distribution for local and remote Clouds</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2016</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE International Conference on Systems, Man, and Cybernetics (SMC)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Budapest, Hungary</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><PAGES><style face="normal" font="default" size="100%">3984-3988</style></PAGES><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Cloud systems include both locally based servers at user premises and remote servers and multiple Clouds that can be reached over the Internet. This paper describes a smart distributed system that combines local and remote Cloud facilities. It operates with a task allocation system that takes decisions to allocate tasks dynamically to the service that offers the best overall Quality of Service and a routing overlay which optimizes network delay for data transfer between clouds. Internet-scale experiments exhibit the effectiveness of our approach in adaptively distributing workload across multiple clouds.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Adaptive workload distribution for local and remote Clouds</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Agreement in Spins and Social Networks</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2016</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">ACM SIGMETRICS Performance Evaluation Review</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">44</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">2</style></ISSUE><PAGES><style face="normal" font="default" size="100%">15-17</style></PAGES><DATE><style face="normal" font="default" size="100%">09/2016</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;We introduce a model of mutually influencing individuals who hold one of two opinions about some matter. This is similar to a system of particles that can have one of two spin states. The model considers N sub-systems where individuals stay for some time, and then move from one subnetwork to another independently of each other, or they may leave the overall network from any one of the sub-systems. They arrive externally to any sub-system according to independent Poisson processes. In each sub-system individuals can influence each other to align with their own opinion or spin, and their opinion can also fluctuate at random in either opinion. The system also allows for a bias or directional field in any of the sub-systems that influences the individuals or spins that are locally present. We show that even with a weak bias, when random fluctuations become small then all the individuals or spins in a given sub-network will align with probability one, to the opinion or spin direction represented by the bias or spin.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Agreement in Spins and Social Networks</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Gokce Gorbil</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Omer Abdelrahman</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">M. Pavloski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Modeling and Analysis of RRC-Based Signalling Storms in 3G Networks</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2016</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE Transactions on Emerging Topics in Computing</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">4</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">1</style></ISSUE><PAGES><style face="normal" font="default" size="100%">113-127</style></PAGES><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Mobile networks are vulnerable to signaling attacks and storms that are caused by traffic patterns that overload the control plane, and differ from distributed denial of service attacks in the Internet since they directly affect the control plane, and also reserve wireless bandwidth and network resources without actually using them. Such storms can result from malware and mobile botnets, as well as from poorly designed applications, and can cause service outages in 3G and 4G networks, which have been experienced by mobile operators. Since the radio resource control (RRC) protocol in the 3G and 4G networks is particularly susceptible to such storms, we analyze their effect with a mathematical model that helps to predict the congestion that is caused by a storm. A detailed simulation model of a mobile network is used to better understand the temporal dynamics of user behavior and signaling in the network and to show how RRC-based signaling attacks and storms cause significant problems in both the control and user planes of the network. Our analysis also serves to identify how storms can be detected, and to propose how system parameters can be chosen to mitigate their effect.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Modeling and Analysis of RRC-Based Signalling Storms in 3G Networks</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>9</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Omer Abdelrahman</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Gokce Gorbil</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ricardo Lent</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Information Sciences and Systems 2015 30th International Symposium on Computer and Information Sciences (ISCIS 2015)</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2016</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Lecture Notes in Electrical Engineering</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><VOLUME><style face="normal" font="default" size="100%">363</style></VOLUME><PAGES><style face="normal" font="default" size="100%">457</style></PAGES><ISBN><style face="normal" font="default" size="100%">978-3-319-22634-7</style></ISBN><title><style face="normal" font="default" size="100%">Information Sciences and Systems 2015 30th International Symposium on Computer and Information Sciences (ISCIS 2015)</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">J. Du</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">C. Jiang</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">H. Zhang</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Y. Ren</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Contract Design for Traffic Offloading and Resource Allocation in Heterogeneous Ultra-Dense Networks</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2017</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE Journal on Selected Areas in Communications</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">35</style></VOLUME><PAGES><style face="normal" font="default" size="100%">2457-2467</style></PAGES><DATE><style face="normal" font="default" size="100%">Nov</style></DATE><title><style face="normal" font="default" size="100%">Contract Design for Traffic Offloading and Resource Allocation in Heterogeneous Ultra-Dense Networks</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Y. Yin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Single-cell based random neural network for deep learning</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2017</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">2017 International Joint Conference on Neural Networks (IJCNN 2017), Anchorage, United States, May 14-19 2017.</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Anchorage, United States</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><VOLUME><style face="normal" font="default" size="100%">2017</style></VOLUME><PAGES><style face="normal" font="default" size="100%">86-93</style></PAGES><DATE><style face="normal" font="default" size="100%">05/2017</style></DATE><title><style face="normal" font="default" size="100%">Single-cell based random neural network for deep learning</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Y. Yin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">L. Wang</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Multi-layer neural networks for quality of service oriented server-state classification in cloud servers</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2017</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">2017 International Joint Conference on Neural Networks (IJCNN 2017), Anchorage, United States, May 14-19 2017.</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Anchorage, United States</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><VOLUME><style face="normal" font="default" size="100%">2017</style></VOLUME><PAGES><style face="normal" font="default" size="100%">1623-1627</style></PAGES><TERTIARY_TITLE><style face="normal" font="default" size="100%">Proceedings of the International Joint Conference on Neural Networks</style></TERTIARY_TITLE><DATE><style face="normal" font="default" size="100%">05/2017</style></DATE><title><style face="normal" font="default" size="100%">Multi-layer neural networks for quality of service oriented server-state classification in cloud servers</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">H. Liu Jiang</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">RK. Thulasiram</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Special Issue on Green Pervasive and Ubiquitous Systems</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2017</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE Systems Journal</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">11</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">2</style></ISSUE><PAGES><style face="normal" font="default" size="100%">806-812</style></PAGES><SECTION><style face="normal" font="default" size="100%">806</style></SECTION><DATE><style face="normal" font="default" size="100%">06/2017</style></DATE><title><style face="normal" font="default" size="100%">Special Issue on Green Pervasive and Ubiquitous Systems</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Y.M. Kadioglu</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Wireless sensor with data and Energy Packets</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2017</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">2017 IEEE International Conference on Communications Workshops  (ICC Workshops 2017)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Paris, France</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">2017</style></DATE><ISBN><style face="normal" font="default" size="100%">978-150901897-0</style></ISBN><title><style face="normal" font="default" size="100%">Wireless sensor with data and Energy Packets</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">E. Pankratova</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">M. Farkhadov</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Research of Heterogeneous Queueing System SM|M(n)|∞</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2017</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Communications in Computer and Information Science 16th International Conference, ITMM 2017</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Kazan, Russian Federation</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">2017</style></DATE><ISBN><style face="normal" font="default" size="100%">978-3-319-68068-2 </style></ISBN><title><style face="normal" font="default" size="100%">Research of Heterogeneous Queueing System SM|M(n)|∞</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">J Fourneau</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">G-networks with Adders</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2017</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Future Internet</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">9</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">3</style></ISSUE><PAGES><style face="normal" font="default" size="100%">1-7</style></PAGES><SECTION><style face="normal" font="default" size="100%">1</style></SECTION><DATE><style face="normal" font="default" size="100%">07/2017</style></DATE><title><style face="normal" font="default" size="100%">G-networks with Adders</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">W. Serrano</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">The Random Neural Network in a neurocomputing application for Web search</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2018</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Neurocomputing</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">280</style></VOLUME><PAGES><style face="normal" font="default" size="100%">123-134 </style></PAGES><SECTION><style face="normal" font="default" size="100%">123</style></SECTION><DATE><style face="normal" font="default" size="100%">03/2018</style></DATE><title><style face="normal" font="default" size="100%">The Random Neural Network in a neurocomputing application for Web search</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">L. Wang</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Adaptive Dispatching of Tasks in the Cloud</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2018</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE Transactions on Cloud Computing </style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">6</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">1</style></ISSUE><PAGES><style face="normal" font="default" size="100%">33-45 </style></PAGES><SECTION><style face="normal" font="default" size="100%">33</style></SECTION><DATE><style face="normal" font="default" size="100%">03/2018</style></DATE><title><style face="normal" font="default" size="100%">Adaptive Dispatching of Tasks in the Cloud</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">W. Serrano</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Intelligent search with deep learning clusters</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2017</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Intelligent Systems Conference (IntelliSys) 2017</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">London, UK </style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE </style></PUBLISHER><DATE><style face="normal" font="default" size="100%">03/2018</style></DATE><ISBN><style face="normal" font="default" size="100%">978-1-5090-6435-9 </style></ISBN><title><style face="normal" font="default" size="100%">Intelligent search with deep learning clusters</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Y Yin</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Deep Learning with Dense Random Neural Networks</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2017</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">International Conference on Man–Machine Interactions</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Kraków, Poland</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer, Cham</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">09/2017</style></DATE><ISBN><style face="normal" font="default" size="100%">978-3-319-67792-7 </style></ISBN><title><style face="normal" font="default" size="100%">Deep Learning with Dense Random Neural Networks</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">W. Serrano</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">The Deep Learning Random Neural Network with a Management Cluster</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2017</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">9th KES International Conference on Intelligent Decision Technologies (KES-IDT 2017)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Vilamoura, Portugal</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer, Cham</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">2017</style></DATE><ISBN><style face="normal" font="default" size="100%">978-3-319-59423-1 </style></ISBN><title><style face="normal" font="default" size="100%">The Deep Learning Random Neural Network with a Management Cluster</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">J Du</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">C Jiang</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">H Zhang</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Z Han</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Y Ren</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Data Transaction Modeling in Mobile Networks: Contract Mechanism and Performance Analysis</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2017</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE Global Telecommunications Conference</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Singapore, Singapore </style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">01/2018</style></DATE><ISBN><style face="normal" font="default" size="100%">978-1-5090-5019-2 </style></ISBN><title><style face="normal" font="default" size="100%">Data Transaction Modeling in Mobile Networks: Contract Mechanism and Performance Analysis</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Mariacarla Staffa</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Luigi Sgaglione</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Giovanni Mazzeo</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Luigi Coppolino</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Salvatore D'Antonio</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Luigi Romano</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">An OpenNCP-based Solution for Secure eHealth Data Exchange</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2018</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">JOURNAL OF NETWORK AND COMPUTER APPLICATIONS</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">116</style></VOLUME><PAGES><style face="normal" font="default" size="100%">65-85</style></PAGES><SECTION><style face="normal" font="default" size="100%">65</style></SECTION><DATE><style face="normal" font="default" size="100%">08/2018</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;The European Commission is very focused on the development of possible solutions to allow effective cross border healthcare provisioning with the aim of guaranteeing a uniform Quality of Service (QoS) level of healthcare systems across Europe. One of the most relevant efforts in this direction was the epSOS Project, with the release of the OpenNCP platform overcoming interoperability issues in health information exchange between nations while complying with both National and European laws. Despite the OpenNCP platform has been adopted in several Member States, allowing them to securely interconnect their eHealth infrastructures, some security issues are still partially addressed, This work shows how the KONFIDO project will address these issues by building on and extending the results of OpenNCP through a sound holistic approach to security at a systemic level. This paper describes in detail the KONFIDO project approach and how it is being deployed via a combination of complementary security enhancing technologies with the ultimate goal of increasing trust and security of eHealth data exchange.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">An OpenNCP-based Solution for Secure eHealth Data Exchange</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Yasin Kadioglu</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Product-Form Solution for Cascade Networks With Intermittent Energy</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2018</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE Systems Journal</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">PP</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">99</style></ISSUE><PAGES><style face="normal" font="default" size="100%">1-10</style></PAGES><SECTION><style face="normal" font="default" size="100%">1</style></SECTION><DATE><style face="normal" font="default" size="100%">08/2018</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;The power needs of digital devices, their installation in locations where it is difficult to connect them to the power grid and the difficulty of frequently replacing batteries, create the need to operate digital systems with harvested energy. In such cases, local storage batteries must overcome the intermittent nature of the energy supply. System performance then depends on the intermittent energy supply, possible energy leakage, and system workload. Queueing networks with product-form solution (PFS) are standard tools for analyzing the performance of interconnected systems, and predicting relevant performance metrics including job queue lengths, throughput, and system turnaround times and delays. However, existing queueing network models assume unlimited energy availability, whereas intermittently harvested energy can affect system performance due to insufficient energy supply. Thus, this paper develops a new PFS for the joint probability distribution of energy availability, and job queue length for an N-node tandem system. Such models can represent production lines in manufacturing systems, supply chains, cascaded repeaters for optical links, or a data link with multiple input data ports that feeds into a switch or server. Our result enables the rigorous computation of the relevant performance metrics of such systems operating with intermittent energy.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Product-Form Solution for Cascade Networks With Intermittent Energy</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Yasin Kadioglu</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Energy life-time of wireless nodes with network attacks and mitigation</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2018</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018; Kansas City; United States</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Kansas City, MO, USA </style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">07/2018</style></DATE><ISBN><style face="normal" font="default" size="100%">978-1-5386-4328-0 </style></ISBN><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;In the Internet of Things (IoT), a simple form of attack can deplete the energy available to operate the sensor nodes. Some of these nodes may use batteries, while others may harvest ambient energy such as photovoltaic, or electromagnetic, or vibration based energy. We first briefly survey the types of attacks which aim at the nodes' energy provisioning systems. This paper analyses the effect of such attacks on the energy life- time of a wireless node. Then we provide models to estimate the effect of attacks that attempt to deplete the node's energy supply, both for a node that uses energy harvesting. We then examine a simple means of attack mitigation based on dropping both attack and «good» traffic. For nodes that use energy harvesting, we compute the fraction of traffic that must be dropped so as to offer a desired «energy life-time» of the node. We see that the required traffic drop rate depends in a non-linear manner on the nominal «good traffic rate» at which the node is expected to operate. Finally, we analyse the impact of attacks on the energy life-time of a node that operates with a replaceable battery&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Energy life-time of wireless nodes with network attacks and mitigation</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Olivier Brun</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Yonghua Yin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Deep Learning with Dense Random Neural Network for Detecting Attacks against IoT-connected Home Environments</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2018</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">The 15th International Conference on Mobile Systems and Pervasive Computing (MobiSPC 2018) / The 13th International Conference on Future Networks and Communications (FNC-2018) / Affiliated Workshops</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Gran Canaria, Spain </style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Elsevier</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">07/2018</style></DATE><ISBN><style face="normal" font="default" size="100%">1877-0509</style></ISBN><ABSTRACT><style face="normal" font="default" size="100%">&lt;div class="author"&gt;Yonghua Yin&lt;/div&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Deep Learning with Dense Random Neural Network for Detecting Attacks against IoT-connected Home Environments</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Joanna Domańska</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Anastasios Drosou</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dimitrios Tzovaras</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Research and innovation action for the security of the internet of things: The SerIoT project</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2018</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%"> Communications in Computer and Information Science</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">London, United Kingdom</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><VOLUME><style face="normal" font="default" size="100%">821</style></VOLUME><PAGES><style face="normal" font="default" size="100%">101-118</style></PAGES><SECTION><style face="normal" font="default" size="100%">101</style></SECTION><ISBN><style face="normal" font="default" size="100%">978-331995188-1</style></ISBN><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;The Internet of Things (IoT) was born in the mid 2010’s, when the threshold of connecting more objects than people to the Internet, was crossed. Thus, attacks and threats on the content and quality of service of the IoT platforms can have economic, energetic and physical security consequences that go way beyond the traditional Internet’s lack of security, and way beyond the threats posed by attacks to mobile telephony. Thus, this paper describes the H2020 project “Secure and Safe Internet of Things” (SerIoT) which will optimize the information security in IoT platforms and networks in a holistic, cross-layered manner (i.e. IoT platforms and devices, honeypots, SDN routers and operator’s controller) in order to offer a secure SerIoT platform that can be used to implement secure IoT platforms and networks anywhere and everywhere.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Research and innovation action for the security of the internet of things: The SerIoT project</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Olivier Brun</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Yonghua Yin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Y. Murat Kadioglu</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Javier Augusto-Gonzalez</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Manuel Ramos</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Deep learning with dense random neural networks for detecting attacks against IoT-connected home environments</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2018</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">1st International Symposia on Computer and Information Sciences, ISCIS 2018</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">London, United Kingdom</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">07/2018</style></DATE><ISBN><style face="normal" font="default" size="100%">978-331995188-1</style></ISBN><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;In this paper, we analyze the network attacks that can be launched against IoT gateways, identify the relevant metrics to detect them, and explain how they can be computed from packet captures. We also present the principles and design of a deep learning-based approach using dense random neural networks (RNN) for the online detection of network attacks. Empirical validation results on packet captures in which attacks were inserted show that the Dense RNN correctly detects attacks.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Deep learning with dense random neural networks for detecting attacks against IoT-connected home environments</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Mariacarla Staffa</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Luigi Coppolino</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Luigi Sgaglione</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ioannis Komnios</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Evangelos Grivas</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Oana Stan</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Luigi Castaldo</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">KONFIDO: An OpenNCP-Based Secure eHealth Data Exchange System</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2018</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">1st International Symposia on Computer and Information Sciences, ISCIS 2018</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">London, United Kingdom</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">07/2018</style></DATE><ISBN><style face="normal" font="default" size="100%">978-3-319-95189-8</style></ISBN><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Allowing cross-border health-care data exchange by establishing a uniform QoS level of health-care systems across European states, represents one of the current main goals of the European Commission. For this purpose epSOS project was funded with the objective to overcome interoperability issues in patients health information exchange among European healthcare systems. A main achievement of the project was the OpenNCP platform. Settled over the results of the epSOS project, KONFIDO aims at increasing trust and security of eHealth data exchange by adopting a holistic approach, as well as at increasing awareness of security issues among the healthcare community. In this light, the paper describes the KONFIDO project’s approach and discusses its design and its representation as a system of interacting agents. It finally discusses the deployment of the provided platform&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">KONFIDO: An OpenNCP-Based Secure eHealth Data Exchange System</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Miltiadis Siavvas</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dionysios Kehagias</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dimitrios Tzovaras</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Static Analysis-Based Approaches for Secure Software Development</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2018</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">1st International Symposia on Computer and Information Sciences, ISCIS 2018</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">London, United Kingdom</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">07/2018</style></DATE><ISBN><style face="normal" font="default" size="100%">978-3-319-95189-8</style></ISBN><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Software security is a matter of major concern for software development enterprises that wish to deliver highly secure software products to their customers. Static analysis is considered one of the most effective mechanisms for adding security to software products. The multitude of static analysis tools that are available provide a large number of raw results that may contain security-relevant information, which may be useful for the production of secure software. Several mechanisms that can facilitate the production of both secure and reliable software applications have been proposed over the years. In this paper, two such mechanisms, particularly the vulnerability prediction models (VPMs) and the optimum checkpoint recommendation (OCR) mechanisms, are theoretically examined, while their potential improvement by using static analysis is also investigated. In particular, we review the most significant contributions regarding these mechanisms, identify their most important open issues, and propose directions for future research, emphasizing on the potential adoption of static analysis for addressing the identified open issues. Hence, this paper can act as a reference for researchers that wish to contribute in these subfields, in order to gain solid understanding of the existing solutions and their open issues that require further research.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Static Analysis-Based Approaches for Secure Software Development</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Joanna Domańska</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Anastasis Drosou</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dimitrios Tzovaras</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Security for Internet of Things: The SerIoT Project</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2018</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">2018 International Symposium on Networks, Computers and Communications (ISNCC)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Rome, Italy</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><title><style face="normal" font="default" size="100%">Security for Internet of Things: The SerIoT Project</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Yunxiao Zhang</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Performance Optimization With Energy Packets</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2019</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE Systems Journal</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><title><style face="normal" font="default" size="100%">Performance Optimization With Energy Packets</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Khaled Hussain</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mohamed Bassyouni</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Accurate, Energy-efficient Classification With Spiking Random Neural Network</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2019</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Probability in the Engineering and Informational Sciences</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><DATE><style face="normal" font="default" size="100%">05/2019</style></DATE><title><style face="normal" font="default" size="100%">Accurate, Energy-efficient Classification With Spiking Random Neural Network</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Miltiadis Siavvas</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Optimum checkpoints for programs with loops</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2019</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Simulation Modelling Practice and Theory</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">97</style></VOLUME><DATE><style face="normal" font="default" size="100%">07/2019</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Checkpoints are widely used to improve the performance of computer systems and programs inthe presence of failures, and they significantly reduce the overall cost of running a program, if the program or the underlying system, are subject to failures. Thus application level checkpointing has been proposed for programs which may execute on platforms which are prone to failures, and also to reduce the execution time of programs which are prone to internal failures. This paper develops a mathematical model to estimate the average execution time of a program in the presence of failures, without and with application level checkpointing, and we use it to predict the optimum interval number of instructions which should be executed between the placement of successive heckpoints. The case of programs with loops and nested loops is also discussed. The results are illustrated with several numerical examples.&#160;&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Optimum checkpoints for programs with loops</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>7</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Enrico Ferrera</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Claudio Pastrone</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Paul-Emmanuel Brun</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Remi De Besombes</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Konstantinos Loupos</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Gerasimos Kouloumpis</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Patrick O’Sullivan</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Alexandros Papageorgiou</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Panayiotis Katsoulakos</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Bill Karakostas</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Antonis Mygiakis</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Christina Stratigaki</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Bora Caglayan</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Basile Starynkevitch</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Christos Skoufis</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Stelios Christofi</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Nicolas Ferry</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Hui Song</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Arnor Solberg</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Peter Matthews</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Antonio Skarmeta</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">José Santa</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Michail Beliatis</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mirko Presser</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Josiane Parreira</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Juan Martínez</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Payam Barnaghi</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Shirin Enshaeifar</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Thorben Iggena</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Marten Fischer</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ralf Tönjes</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Martin Strohbach</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Alessandro Sforzin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Hien Truong</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">John Soldatos</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Sofoklis Efremidis</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Georgios Koutalieris</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Panagiotis Gouvas</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Juergen Neises</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">George Hatzivasilis</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ioannis Askoxylakis</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Vivek Kulkarni</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Arne Broering</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dariusz Dober</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Kostas Ramantas</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Christos Verikoukis</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Joachim Posegga</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Domenico Presenza</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">George Spanoudakis</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Danilo Pau</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Sławomir Nowak</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mateusz Nowak</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Joanna Domańska</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Anastasis Drosou</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dimitrios Tzovaras</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tommi Elo</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Santeri Paavolainen</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dmitrij Lagutin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Helen Leligou</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Panagiotis Trakadas</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">George Polyzos</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">IoT European Security and Privacy Projects: Integration, Architectures and Interoperability</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2018</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Next Generation Internet of Things. Distributed Intelligence at the Edge and Human Machine-to-Machine Cooperation</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">River Publishers</style></PUBLISHER><SECTION><style face="normal" font="default" size="100%">7</style></SECTION><ISBN><style face="normal" font="default" size="100%">9788770220071</style></ISBN><title><style face="normal" font="default" size="100%">IoT European Security and Privacy Projects: Integration, Architectures and Interoperability</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Piotr Fröhlich</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Optimal fog services placement in SDN IoT network using Random Neural Networks and Cognitive Network Map</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2020</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">The 19th International Conference on Artificial Intelligence and Soft Computing</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Zakopane</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">06/2020</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Due to massive increase in number of &lt;em&gt;IoT&lt;/em&gt;&#160;devices and number of cloud based services a crucial task arises of optimally placing (both topologically and resource-wise ) services in the network so that non of the clients will be victimized and receive best possible time of response. Also - there must be a balance not to instantiate a service on every possible machine - which would take too much resources. Task which must be solved is an optimization of parameters such as &lt;em&gt;QoS&lt;/em&gt;&#160;between service and client, equality of clients and usage of resources. Using &lt;em&gt;SDN&lt;/em&gt;&#160;- which is designed to answer some of the problems posed in this section such as &lt;em&gt;QoS&lt;/em&gt;&#160;and knowledge about topology of the whole network and newly connected clients - is a gateway to better fit service management. Machine learning provides less stiff rules to follow and more intelligent behaviour of the manager.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Optimal fog services placement in SDN IoT network using Random Neural Networks and Cognitive Network Map</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piotr Fröhlich</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mateusz Nowak</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Stavros Papadopoulos</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Aikaterini Protogerou</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Anastasis Drosou</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dimitrios Tzovaras</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">IoT Network Attack Detection and Mitigation</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2020</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">The 9th Mediterranean Conference on Embedded Computing (MECO'2020)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Budva, Montenegro</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><DATE><style face="normal" font="default" size="100%">June 2020</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">Cyberattacks on the Internet of Things (IoT) can cause major economic and physical damage, and disrupt production lines, manufacturing processes, supply chains, impact the physical safety of vehicles, and damage the health of human beings. Thus we describe and evaluate a distributed and robust attack detection and mitigation system for network environments where communicating decision agents use Graph Neural Networks to provide attack alerts. We also present an attack mitigation system that uses a Reinforcement Learning driven Software Defined Network to process the alerts generated by the attack detection system, together with Quality of Service measurements, so as to re-route sensitive traffic away from compromised network paths using. Experimental results illustrate both the detection and re-routing scheme.&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%"/></URL><title><style face="normal" font="default" size="100%">IoT Network Attack Detection and Mitigation</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">M. Pavloski</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Performance of a Security Control Scheme for a Health Data Exchange System</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2020</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%"> IEEE International Black Sea Conference on Communications and Networking</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">05/2020</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;We propose an access control scheme to improve the security of a cross border health data access running under distributed systems such as OpenNCP that cooperate through message exchanges. The scheme allows messages that request data and services to cross different regional or national nodes after they are verified for security issues both by the sender and the receiver. It rejects those access requests that are detected to be malicious, and consequently throttles down the flow of permit messages via explicit feedback from threat detection software placed at the sender and the receiver. An analytical model is used to evaluate the message delay to determine the overhead caused by the enhanced security system, and numerical results illustrate the impact of attack rates and on the system’s performance.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Performance of a Security Control Scheme for a Health Data Exchange System</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>7</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Gianmarco Baldini</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piotr Fröhlich</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jose Hernandez-Ramos</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mateusz Nowak</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Sławomir Nowak</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Stavros Papadopoulos</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Anastasios Drosou</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dimitrios Tzovaras</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">IoT Network Risk Assessment and Mitigation: The  SerIoT Approach</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2020</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Security Risk Management for the Internet of Things: Technologies and Techniques  for IoT Security, Privacy and Data Protection</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Now Publishers</style></PUBLISHER><PAGES><style face="normal" font="default" size="100%">88–104</style></PAGES><EDITION><style face="normal" font="default" size="100%">John Soldatos</style></EDITION><title><style face="normal" font="default" size="100%">IoT Network Risk Assessment and Mitigation: The  SerIoT Approach</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Joanna Domańska</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piotr Fröhlich</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mateusz Nowak</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Sławomir Nowak</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Self-Aware Networks that Optimize Security, QoS and Energy</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2020</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Proceedings of the IEEE</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">108</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">7</style></ISSUE><DATE><style face="normal" font="default" size="100%">July 2020</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">The need to adaptively manage computer systems and networks so as to offer good Quality of Service (QoS) and Quality of Experience (QoE), with secure operation at relatively low levels of energy consumption is challenged by their sheer complexity and the wide variability of the workloads. A possible way forward is through self-awareness, whereby self-measurement and self-observation, together with on-line control mechanisms which operate adaptively to attain the required performance and QoE. We survey the premises for these ideas coming from Cognitive Science and Active Networks, and review recent work on self-aware computer systems and networks, including those that propose the use of Software Defined Networks as a means to implement these concepts. Then we provide some examples from the literature on self-aware systems to illustrate the performance gains that they can provide. Finally we detail an example system and its working algorithms to allow the reader to understand how such a system may be implemented. Measurements showing how it can react rapidly to changing network conditions regarding Quality of Service (QoS) and security are also presented. Some conclusions and suggestions for further work are also presented&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/abstract/document/9103525</style></URL><title><style face="normal" font="default" size="100%">Self-Aware Networks that Optimize Security, QoS and Energy</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Piotr Fröhlich</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mateusz Nowak</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Smart SDN Management of Fog Services</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2020</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Global IoT Summit (GIOTS) 2020</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Dublin, Ireland</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">June 2020</style></DATE><ISBN><style face="normal" font="default" size="100%">978-1-7281-6728-2</style></ISBN><ABSTRACT><style face="normal" font="default" size="100%">A Smart Service Manager is proposed to direct user requests (such as those coming from IoT devices) at the edge&#13;
towards appropriate servers where the requested services can be satisfied. Services are housed at different Fog locations, and the system is subject to variations in workload. The approach is based on using a Software Defined Network (SDN) controller as the means to take decisions with measurement data based machine learning that uses Reinforcement Learning to make the best choices. The system we have developed is illustrated with experimental results on a test-bed with time-varying loads that confirm its ability to adapt to significant changes in system load and preserve the users’ Quality of Service.</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/9119542</style></URL><title><style face="normal" font="default" size="100%">Smart SDN Management of Fog Services</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Introduction to the Special Issue on the French-Polish Collaboration in Mathematical Models of Computer Systems, Networks and Bioinformatics</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2020</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%"> SN Computer Science</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">1</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">1</style></ISSUE><PAGES><style face="normal" font="default" size="100%">44:1-44:10</style></PAGES><title><style face="normal" font="default" size="100%">Introduction to the Special Issue on the French-Polish Collaboration in Mathematical Models of Computer Systems, Networks and Bioinformatics</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>7</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Yunxiao Zhang</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Sharing Energy for Optimal Edge Performance</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2020</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">SOFSEM 2020: Theory and Practice of Computer Science</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Cham</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><NUMBER><style face="normal" font="default" size="100%"> Lecture Notes in Computer Science, vol 12011</style></NUMBER><EDITION><style face="normal" font="default" size="100%">Chatzigeorgiou A. et al.</style></EDITION><ABSTRACT><style face="normal" font="default" size="100%">Using the Energy Packet Network (EPN) model, we show how energy can be shared between heterogeneous servers at the edge to minimize the overall average response time of jobs. The system is modelled as a probabilistic network where energy and jobs are being dispatched to the edge servers using G-Networks with a product-form solution for the equilibrium probability distribution of system state. The approach can also be used to design energy dispatching systems when renewable energy is used to improve the sustainability of edge computing.</style></ABSTRACT><title><style face="normal" font="default" size="100%">Sharing Energy for Optimal Edge Performance</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Marco Nalin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ilaria Baroni</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Giuliana Faiella</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Maria Romano</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Flavia Matrisciano</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">David Martinez</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jos Dumortier</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Pantelis Natsiavas</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Kostas Votis</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Vassilis Koutkias</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dimitrios Tzovaras</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Fabrizio Clemente</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">The European cross-border health data exchange roadmap: Case study in the Italian setting</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2019</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">J. 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Informatics</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">94</style></VOLUME><URL><style face="normal" font="default" size="100%">https://doi.org/10.1016/j.jbi.2019.103183</style></URL><title><style face="normal" font="default" size="100%">The European cross-border health data exchange roadmap: Case study in the Italian setting</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Rajkumar Buyya</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Satish Srirama</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Giuliano Casale</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Rodrigo Calheiros</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Yogesh Simmhan</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Blesson Varghese</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Bahman Javadi</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Luis Vaquero</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Marco Netto</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Adel Toosi</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Maria Rodriguez</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ignacio Llorente</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Sabrina di Vimercati</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Pierangela Samarati</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dejan Milojicic</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Carlos Varela</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Rami Bahsoon</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Marcos de Assunção</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Omer Rana</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Wanlei Zhou</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Hai Jin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Wolfgang Gentzsch</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Albert Zomaya</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Haiying Shen</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">A Manifesto for Future Generation Cloud Computing: Research Directions for the Next Decade</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2019</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">ACM Comput. 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Information Forensics and Security</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">14</style></VOLUME><PAGES><style face="normal" font="default" size="100%">1582–1594</style></PAGES><URL><style face="normal" font="default" size="100%">https://doi.org/10.1109/TIFS.2018.2883000</style></URL><title><style face="normal" font="default" size="100%">Peer Prediction-Based Trustworthiness Evaluation and Trustworthy Service Rating in Social Networks</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Jun Du</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Chunxiao Jiang</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Haijun Zhang</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Yong Ren</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tony Quek</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Double Auction Mechanism Design for Video Caching in Heterogeneous Ultra-Dense Networks</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2019</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE Trans. 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Ghavami</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Melanie Volkamer</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piroska Haller</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">A. Sánchez</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">M. Dimas</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Paolo Campegiani</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Sokratis Katsikas</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ioannis Komnios</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Luigi Romano</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dimitrios Tzovaras</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">GHOST - Safe-Guarding Home IoT Environments with Personalised Real-Time Risk Control</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2018</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Security in Computer and Information Sciences - First International ISCIS Security Workshop 2018, Euro-CYBERSEC 2018, London, UK, February 26-27, 2018, Revised Selected Papers</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><URL><style face="normal" font="default" size="100%">https://doi.org/10.1007/978-3-319-95189-8_7</style></URL><title><style face="normal" font="default" size="100%">GHOST - Safe-Guarding Home IoT Environments with Personalised Real-Time Risk Control</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Ingrid Grenet</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Yonghua Yin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jean-Paul Comet</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Vera Kurková</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Yannis Manolopoulos</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Barbara Hammer</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Lazaros Iliadis</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ilias Maglogiannis</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Machine Learning to Predict Toxicity of Compounds</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2018</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Artificial Neural Networks and Machine Learning - ICANN 2018 - 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part I</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><URL><style face="normal" font="default" size="100%">https://doi.org/10.1007/978-3-030-01418-6_33</style></URL><title><style face="normal" font="default" size="100%">Machine Learning to Predict Toxicity of Compounds</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Yasin Kadioglu</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Energy Life-Time of Wireless Nodes with Network Attacks and Mitigation</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2018</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018, Kansas City, MO, USA, May 20-24, 2018</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><URL><style face="normal" font="default" size="100%">https://doi.org/10.1109/ICCW.2018.8403561</style></URL><title><style face="normal" font="default" size="100%">Energy Life-Time of Wireless Nodes with Network Attacks and Mitigation</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Olumide Akinwande</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Krzysztof Grochla</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ricardo Lent</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">A Reinforcement Learning Approach to Adaptive Forwarding in Named Data Networking</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2018</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Computer and Information Sciences - 32nd International Symposium, ISCIS 2018, Held at the 24th IFIP World Computer Congress, WCC 2018, Poznan, Poland, September 20-21, 2018, Proceedings</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><URL><style face="normal" font="default" size="100%">https://doi.org/10.1007/978-3-030-00840-6_23</style></URL><title><style face="normal" font="default" size="100%">A Reinforcement Learning Approach to Adaptive Forwarding in Named Data Networking</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Jun Du</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Chunxiao Jiang</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Zhu Han</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Yong Ren</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mohsen Guizani</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Networked Data Transaction in Mobile Networks: A Prediction-based Approach Using Auction</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2018</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%"/></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><URL><style face="normal" font="default" size="100%">https://doi.org/10.1109/IWCMC.2018.8450531</style></URL><title><style face="normal" font="default" size="100%">Networked Data Transaction in Mobile Networks: A Prediction-based Approach Using Auction</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Jun Du</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Chunxiao Jiang</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Zhu Han</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Yong Ren</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mohsen Guizani</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Cognitive Data Allocation for Auction-based Data Transaction in Mobile Networks</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2018</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%"/></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><URL><style face="normal" font="default" size="100%">https://doi.org/10.1109/IWCMC.2018.8450428</style></URL><title><style face="normal" font="default" size="100%">Cognitive Data Allocation for Auction-based Data Transaction in Mobile Networks</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Paolo Campegiani</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Sokratis Katsikas</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ioannis Komnios</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Luigi Romano</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dimitrios Tzovaras</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Security in Computer and Information Sciences - First International ISCIS Security Workshop 2018, Euro-CYBERSEC 2018, London, UK, February 26-27, 2018, Revised Selected Papers</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2018</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Communications in Computer and Information Science</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><VOLUME><style face="normal" font="default" size="100%">821</style></VOLUME><ISBN><style face="normal" font="default" size="100%">978-3-319-95188-1</style></ISBN><URL><style face="normal" font="default" size="100%">https://doi.org/10.1007/978-3-319-95189-8</style></URL><title><style face="normal" font="default" size="100%">Security in Computer and Information Sciences - First International ISCIS Security Workshop 2018, Euro-CYBERSEC 2018, London, UK, February 26-27, 2018, Revised Selected Papers</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Krzysztof Grochla</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ricardo Lent</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Computer and Information Sciences - 32nd International Symposium, ISCIS 2018, Held at the 24th IFIP World Computer Congress, WCC 2018, Poznan, Poland, September 20-21, 2018, Proceedings</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2018</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Communications in Computer and Information Science</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><VOLUME><style face="normal" font="default" size="100%">935</style></VOLUME><ISBN><style face="normal" font="default" size="100%">978-3-030-00839-0</style></ISBN><title><style face="normal" font="default" size="100%">Computer and Information Sciences - 32nd International Symposium, ISCIS 2018, Held at the 24th IFIP World Computer Congress, WCC 2018, Poznan, Poland, September 20-21, 2018, Proceedings</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Godlove Kuaban</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dariusz Marek</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Transient Behaviour of a Network Router</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2020</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">43th International Conference on Telecommunications and Signal Processing</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Milano, Italy</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;The broader use of Software Defined Network (SDN) controllers creates periodic changes in topology and traffic rates at routers that adapt the network to changes in network conditions.&#160;&lt;br /&gt;&#13;
Thus the transient behaviour of network components, and in particular routers, is becoming of great interest.&lt;br /&gt;&#13;
Since standard queueing models are difficult to analyze under time-varying conditions, we propose a tractable diffusion approximation for both the transient and steady-state behaviour of a network router. In particular, the analysis provides the steady-state and transient delay and packet loss probability as a function of traffic load and other characteristics. Using these results, we show that when SDN routers change the paths of flows frequently, the network's behaviour may often be far from its steady-state behaviour. Therefore any network optimization conducted with the help of SDN should not be based on&#160;steady-state behaviour, but rather on some metric reated to the network time dependent behaviour.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Transient Behaviour of a Network Router</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Huibo Bi</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">A Survey of Algorithms and Systems for Evacuating People in Confined Spaces</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2019</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Electronics — Open Access Journal</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%"> vol. 8</style></VOLUME><SECTION><style face="normal" font="default" size="100%"> vol. 8, p. 711</style></SECTION><DATE><style face="normal" font="default" size="100%">06/2019</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;The frequency, destruction and costs of natural and human-made disasters in modern&lt;br /&gt;&#13;
highly-populated societies have resulted in research on emergency evacuation and wayfinding, which&lt;br /&gt;&#13;
has drawn considerable attention. The subject is now a multidisciplinary area of research where&lt;br /&gt;&#13;
information and communication technologies (ICT), and in particular the Internet of Things (IoT),&lt;br /&gt;&#13;
have a significant impact on sensing and computing dynamic reactions that mitigate or prevent&lt;br /&gt;&#13;
the worst outcomes of disasters. This paper offers state-of-the-art knowledge in this area so as to&lt;br /&gt;&#13;
share ongoing research results, identify the research gaps and address the need for future research.&lt;br /&gt;&#13;
We present a comprehensive review of research on emergency evacuation and wayfinding, focusing&lt;br /&gt;&#13;
on the algorithmic and system design aspects. Starting from the history of emergency management&lt;br /&gt;&#13;
research, we identify the emerging challenges concerning system optimisation, evacuee behaviour&lt;br /&gt;&#13;
optimisation and data analysis, and the additional energy consumption by ICT equipment that&lt;br /&gt;&#13;
operates the emergency management infrastructure.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">A Survey of Algorithms and Systems for Evacuating People in Confined Spaces</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Huibo Bi</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Yanyan Chen</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Incentive mechanism for collective coordination in an urban intelligent transportation system using G-networks</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2020</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE MASCOTS 2020</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Nice France</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEEXpress</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">10/2020</style></DATE><title><style face="normal" font="default" size="100%">Incentive mechanism for collective coordination in an urban intelligent transportation system using G-networks</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Miltiadis Siavvas</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Paweł Boryszko</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Joanna Domańska</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Optimum checkpoints for time and energy</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2020</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE MASCOTS 2020</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Nice France</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEEXpress</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">10/2020</style></DATE><URL><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/9285959</style></URL><title><style face="normal" font="default" size="100%">Optimum checkpoints for time and energy</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Will Serrano</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Yonghua Yin</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">The Random Neural Network with Deep Learning Clusters in Smart Search</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2020</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Neurocomputing</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">396</style></VOLUME><SECTION><style face="normal" font="default" size="100%">394</style></SECTION><DATE><style face="normal" font="default" size="100%">04/2020</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;This paper proposes a Neurocomputing application that reorders the Web results obtained from different Web Search Engines emulating the way our brain takes decisions. The proposed application is based on the Random Neural Network with Deep Learning Clusters that evaluates and adapts Web result relevance by associating independently each Deep Learning Cluster to a specific Web Search Engine. In addition, this paper presents a Deep Learning Cluster to perform as a Management Cluster that decides the final result relevance based on the inputs from each independent Deep Learning cluster. The performance of the proposed Management Cluster is evaluated when included as an additional layer to the Deep Learning Clusters. On average; the proposed Deep Learning cluster structure improves Smart Search performance&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">The Random Neural Network with Deep Learning Clusters in Smart Search</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Godlove Kuaban</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dariusz Marek</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Time Dependent Diffusion Model for Security Driven Software Defined Networks</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2020</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Second International Workshop on Stochastic Modeling and Applied Research of Technology (SMARTY 2020)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Petrozavodsk, Karelia, Russia</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">CEUR-WS.org</style></PUBLISHER><VOLUME><style face="normal" font="default" size="100%">2792</style></VOLUME><PAGES><style face="normal" font="default" size="100%">38-56</style></PAGES><EDITION><style face="normal" font="default" size="100%">CEUR Workshop Proceedings</style></EDITION><DATE><style face="normal" font="default" size="100%">12/2020</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;We present a model of a Software Defined Network (SDN) where frequent changes in routing and traffic rates at routers are needed to respond to the security, quality of service (QoS), and energy savings requirements of applications such as the Internet of Things.&lt;br /&gt;&#13;
Such frequent path and traffic changes introduce time-dependent network behaviours, and standard queueing models are not well adapted to analyse the transient regime, we propose a tractable diffusion approximation for both the transient and steady-state behaviour.&lt;br /&gt;&#13;
Our model can represent any network topology transmitting time-dependent flows with routing changes, and computes queue length and delay distributions at each network node and along&#160;complete paths between senders and receivers.&#160;&lt;br /&gt;&#13;
Using realistic router parameters, we show that transients occupy a significant fraction of system time, so that the optimisation conducted with SDN controllers needs to include the effect of time-dependent behaviours.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-2792/</style></URL><title><style face="normal" font="default" size="100%">Time Dependent Diffusion Model for Security Driven Software Defined Networks</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Will Serrano</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Deep Learning Clusters in the Cognitive Packet Network</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2020</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Neurocomputing</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">396</style></VOLUME><SECTION><style face="normal" font="default" size="100%">406</style></SECTION><DATE><style face="normal" font="default" size="100%">07/2020</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;The Cognitive&#160;&lt;a href="https://www.sciencedirect.com/topics/computer-science/packet-network" title="Learn more about Packet Network from ScienceDirect's AI-generated Topic Pages"&gt;Packet Network&lt;/a&gt;&#160;(CPN) bases its routing decisions and flow control on the Random&#160;&lt;a href="https://www.sciencedirect.com/topics/neuroscience/neural-networks" title="Learn more about Neural Networks from ScienceDirect's AI-generated Topic Pages"&gt;Neural Network&lt;/a&gt;&#160;(RNN) Reinforcement&#160;&lt;a href="https://www.sciencedirect.com/topics/computer-science/learning-algorithm" title="Learn more about Learning Algorithm from ScienceDirect's AI-generated Topic Pages"&gt;Learning algorithm&lt;/a&gt;; this paper proposes the addition of a Deep Learning (DL) Cluster management structure to the CPN for Quality of Service metrics (Delay Loss and Bandwidth), Cyber Security keys (User, Packet and Node) and Management decisions (QoS, Cyber and CEO). The RNN already models how neurons transmit information using positive and negative impulsive signals whereas the proposed additional Deep Learning structure emulates the way the brain learns and takes decisions; this paper presents a brain model as the combination of both learning algorithms, RNN and DL. The proposed model has been simulated under different network sizes and scenarios and it has been validated against the CPN itself without DL clusters. The simulation results are promising; the presented CPN with DL clusters as a mechanism to transmit, learn and make packet routing decisions is a step closer to emulate the way the brain transmits information, learns the environment and takes decisions.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Deep Learning Clusters in the Cognitive Packet Network</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Jun Du</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Chunxiao Jiang</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Zhu Han</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Yong Ren</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Auction-Based Data Transaction in Mobile Networks: Data Allocation Design and Performance Analysis</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2020</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE Transactions on Mobile Computing</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">19</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">5</style></ISSUE><SECTION><style face="normal" font="default" size="100%">1040</style></SECTION><DATE><style face="normal" font="default" size="100%">05/2020</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Mobile data traffic is experiencing unprecedented increases due to the proliferation of highly capable smartphones, laptops and tablets, and mobile data offloading can be used to move traffic from cellular networks to other wireless infrastructures such as small-cell base stations. This work addresses the related issue of data allocation, by proposing a novel infrastructure independent method based on the hotspot function of smartphones. In the proposed scheme, smartphones transfer data allowances among mobile users, so that users with excess data allowances act as accessible Wi-Fi hotspots, selling their data allowance to other users who need extra data allowances. To achieve this objective, we propose to use auctions with single and multiple data sellers. Efficient schemes based on auction models are discussed to sell the data allowances over successive days in a month, and over different time slots during a single day. Overall system performance is considered based on the behavior of mobile users, such as changing demands for the sale or purchase of data allowances. Together with the analytical results presented, our simulation experiments also indicate that knowledge of user behavior can significantly improve the performance of data allowance transactions, leading to highly efficient allocations among users.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Auction-Based Data Transaction in Mobile Networks: Data Allocation Design and Performance Analysis</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dariusz Marek</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Software Defined Network Dynamics via Diffusions</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2020</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">28th International Symposium on Modelling, Analysis, and Simulation of Computer and Telecommunication Systems, MASCOTS 2020, revised delected papers</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">on-line</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer International Publishing</style></PUBLISHER><VOLUME><style face="normal" font="default" size="100%">12527</style></VOLUME><EDITION><style face="normal" font="default" size="100%">Computer Communication Networks and Telecommunications, LNCS</style></EDITION><DATE><style face="normal" font="default" size="100%">12/2020</style></DATE><ISBN><style face="normal" font="default" size="100%">978-3-030-68110-4</style></ISBN><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Software-Defined Networks (SDN) dynamically modify the paths of Internet flows in response to the quality of service or security needs, and hence frequently modify traffic levels at network routers. Thus network routers often operate in the transient regime, rather than at steady-state, with significant impact on packet loss probabilities and delay. We, therefore, investigate the time-dependent performance of a small network of routers, modelled as G/G/1/N queueing stations. &#160;A diffusion approximation is developed to predict the quality of service of the routers in the transient regime. Numerical examples show that&#160;the results in the transient regime can differ very significantly from the steady-state results, and therefore that the&#160;transient analysis must be taken into account in evaluating the performance of routers in a SDN network.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://www.springer.com/gp/book/9783030681098</style></URL><title><style face="normal" font="default" size="100%">Software Defined Network Dynamics via Diffusions</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Katarzyna Filus</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Miltiadis Siavvas</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Joanna Domańska</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">The Random Neural Network as a Bonding Model for Software Vulnerability Prediction</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Modelling, Analysis, and Simulation of Computer and Telecommunication Systems</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer International Publishing</style></PUBLISHER><VOLUME><style face="normal" font="default" size="100%">12527</style></VOLUME><PAGES><style face="normal" font="default" size="100%">102-116</style></PAGES><TERTIARY_TITLE><style face="normal" font="default" size="100%">Computer Communication Networks and Telecommunications, LNCS</style></TERTIARY_TITLE><URL><style face="normal" font="default" size="100%">https://link.springer.com/chapter/10.1007/978-3-030-68110-4_7</style></URL><title><style face="normal" font="default" size="100%">The Random Neural Network as a Bonding Model for Software Vulnerability Prediction</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Katarzyna Filus</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Joanna Domańska</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Random Neural Network for Lightweight Attack Detection in the IoT</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">MASCOTS 2020: Modelling, Analysis, and Simulation of Computer and Telecommunication Systems</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer International Publishing</style></PUBLISHER><VOLUME><style face="normal" font="default" size="100%">12527</style></VOLUME><PAGES><style face="normal" font="default" size="100%">79-91</style></PAGES><TERTIARY_TITLE><style face="normal" font="default" size="100%">Computer Communication Networks and Telecommunications, LNCS</style></TERTIARY_TITLE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Cyber-attack detection has become a basic component of all information processing systems, and once an attack is detected it may be possible to block or mitigate its effects. This paper addresses the use of a learning recurrent Random Neural Network (RNN) to build a lightweight detector for certain types of Botnet attacks on IoT systems. Its low computational cost based on a small 12-neuron recurrent architecture makes it particularly attractive for edge devices. The RNN can be trained off-line using a fast simplified gradient descent algorithm, and we show that it can lead to high detection rates of the order of 96%, with false alarm rates of a few percent.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://link.springer.com/chapter/10.1007/978-3-030-68110-4_5</style></URL><title><style face="normal" font="default" size="100%">Random Neural Network for Lightweight Attack Detection in the IoT</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Katarzyna Filus</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Paweł Boryszko</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Joanna Domańska</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Miltiadis Siavvas</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Efficient Feature Selection for Static Analysis Vulnerability Prediction</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Sensors</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">21 (4)</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">Special Issue: Security and Privacy in Software Based Critical Contexts</style></ISSUE><URL><style face="normal" font="default" size="100%">https://www.mdpi.com/1424-8220/21/4/1133</style></URL><title><style face="normal" font="default" size="100%">Efficient Feature Selection for Static Analysis Vulnerability Prediction</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Godlove Kuaban</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dariusz Marek</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Time Dependent Diffusion Model for Security Driven Software Defined Networks</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2020</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">The Second International Workshop on Stochastic Modeling and Applied Research of Technology (SMARTY 2020)</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">2792</style></VOLUME><PAGES><style face="normal" font="default" size="100%">pp. 38-56</style></PAGES><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;We present a model of a Software Defined Network (SDN) where frequent changes in routing and traffic rates at routers are needed&lt;br /&gt;&#13;
to respond to the security, quality of service (QoS), and energy savings requirements of applications such as the Internet of Things. Such frequent path and traffic changes introduce time-dependent network behaviours, and standard queueing models are not well adapted to analyse the transient regime, we propose a tractable diffusion approximation for both the transient and steady-state behaviour. Our model can represent any network topology transmitting time-dependent flows with routing changes, and computes queue length and delay distributions at each network node and along complete paths between senders and receivers. Using realistic router parameters, we show that transients occupy a significant fraction of system time, so that the optimisation conducted with SDN controllers needs to include the effect of time-dependent behaviours.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Time Dependent Diffusion Model for Security Driven Software Defined Networks</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Godlove Kuaban</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dariusz Marek</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Time-Dependent Performance of a Multi-Hop Software Defined Network</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Applied Sciences</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">11</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">2469</style></ISSUE><SECTION><style face="normal" font="default" size="100%">1-21</style></SECTION><DATE><style face="normal" font="default" size="100%">03/2021</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;It has been recently observed that Software Defined Networks (SDN) can change the&lt;br /&gt;&#13;
paths of different connections in the network at a relatively frequent pace to improve the overall&lt;br /&gt;&#13;
network performance, including delay and packet loss, or to respond to other needs such as security.&lt;br /&gt;&#13;
These changes mean that a network that SDN controls will seldom operate in steady state; rather,&lt;br /&gt;&#13;
the network may often be in transient mode, especially when the network is heavily loaded and&lt;br /&gt;&#13;
path changes are critically important. Hence, we propose a transient analysis of such networks to&lt;br /&gt;&#13;
better understand how frequent changes in paths and the switches’ workloads may affect multi-hop&lt;br /&gt;&#13;
networks’ performance. Since conventional queueing models are difficult to solve for transient&lt;br /&gt;&#13;
behaviour and simulations take excessive computation time due to the need for statistical accuracy,&lt;br /&gt;&#13;
we use a diffusion approximation to study a multi-hop network controlled by SDN. The results show&lt;br /&gt;&#13;
that network optimization should consider the transient effects of SDN and that transients need to be&lt;br /&gt;&#13;
included in the design of algorithms for SDN controllers that optimize network performance.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://www.mdpi.com/journal/applsci</style></URL><title><style face="normal" font="default" size="100%">Time-Dependent Performance of a Multi-Hop Software Defined Network</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Dionysios Kehagias</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Marija Jankovic</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Miltiadis Siavvas</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Investigating the Interaction between Energy Consumption, Quality of Service, Reliability, Security, and Maintainability of Computer Systems and Networks</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">SN Computer Science </style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">2</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">1</style></ISSUE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;The Conference on Energy Consumption, Quality of Service, Reliability, Security, and Maintainability of Computer Systems and Networks (EQSEM) was held as a virtual conference on October 20–21, 2020. This paper summarises the objectives and proceedings of this conference. It then briefly presents the keynotes and other papers which were presented. Then, in the context of the EU-funded research project SDK4ED which motivated this conference, we outline several solutions that are being developed for managing the potential inter-dependencies and corresponding trade-offs between design-time and runtime qualities in software applications, and review the key functionalities that have been implemented within the SDK4ED integrated platform as of this writing. Then, we briefly introduce four papers among those presented at the EQSEM Conference that are included in this issue of the journal SN Computer Science, presenting relevant research achievements of the SDK4ED Project on software maintainability, reliability, and energy efficiency.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Investigating the Interaction between Energy Consumption, Quality of Service, Reliability, Security, and Maintainability of Computer Systems and Networks</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Miltiadis Siavvas</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Minimizing Energy and Computation in Long-Running Software</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Applied Sciences</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">11</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">3</style></ISSUE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Long-running software may operate on hardware platforms with limited energy resources&lt;br /&gt;&#13;
such as batteries or photovoltaic, or on high-performance platforms that consume a large amount&lt;br /&gt;&#13;
of energy. Since such systems may be subject to hardware failures, checkpointing is often used to&lt;br /&gt;&#13;
assure the reliability of the application. Since checkpointing introduces additional computation time&lt;br /&gt;&#13;
and energy consumption, we study how checkpoint intervals need to be selected so as to minimize a&lt;br /&gt;&#13;
cost function that includes the execution time and the energy. Expressions for both the program’s&lt;br /&gt;&#13;
energy consumption and execution time are derived as a function of the failure probability per&lt;br /&gt;&#13;
instruction. A first principle based analysis yields the checkpoint interval that minimizes a linear&lt;br /&gt;&#13;
combination of the average energy consumption and execution time of the program, in terms of the&lt;br /&gt;&#13;
classical “Lambert function”. The sensitivity of the checkpoint to the importance attributed to energy&lt;br /&gt;&#13;
consumption is also derived. The results are illustrated with numerical examples regarding programs&lt;br /&gt;&#13;
of various lengths and showing the relation between the checkpoint interval that minimizes energy&lt;br /&gt;&#13;
consumption and execution time, and the one that minimizes a weighted sum of the two. In addition,&lt;br /&gt;&#13;
our results are applied to a popular software benchmark, and posted on a publicly accessible web&lt;br /&gt;&#13;
site, together with the optimization software that we have developed.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Minimizing Energy and Computation in Long-Running Software</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Guy Brasseur</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Luc Cheffneux</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Véronique Dehant</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Véronique Halloin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jean-Paul Haton</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Michel Judkiewicz</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Bernard Rentier</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Romain Weikmans</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Réflexions sur la diffusion des connaissances a travers les grands colloques internationaux, les revues scientifiques, et la communication libre et rapide entre chercheurs et innovateurs dans un contexte de reduction de l'empreinte climatique</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">1024, Bulletin de la Société Informatique de France</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">17</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">1</style></ISSUE><SECTION><style face="normal" font="default" size="100%">155-265</style></SECTION><DATE><style face="normal" font="default" size="100%">04/2021</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">Au début de l’année 2019, la classe Technologie et société de l’Académie royale de Belgique a suivi la proposition de son directeur d’orienter principalement ses travaux, durant deux ans, sur le thème de la Transition vers un futur désirable. Personne à ce moment n’imaginait que nous allions vivre une crise de l’ampleur de celle que nous traversons. La pandémie révèle le manque de résilience du monde dans lequel nous vivons ; elle en souligne particulièrement la complexité tout comme les interactions multiples qui lient les douze domaines que la classe avait souhaité aborder dans sa démarche. Si la santé est concernée au premier chef par la crise sanitaire, le monde de l’entreprise et du travail n’a pas manqué d’être directement impacté. Notre modèle économique et financier a été interpelé et la nécessité fut relevée de disposer d’infrastructures de bon niveau de même que d’une gouvernance capable de faire face à des situations critiques qui peuvent générer des risques touchant à la paix et à la sécurité. Il est aussi apparu combien les valeurs de solidarité, de civisme et de cohésion sociale retrouvent toute leur importance, tandis que la nécessité des connaissances scientifiques et de l’expertise est mise en exergue. Les technologies disruptives (en particulier le numérique) nous offrent la possibilité de garder le contact, tout en suscitant parfois de sérieux problèmes de respect de la vie privée et des droits humains. Notre système d’éducation s’en trouve lourdement perturbé et l’impact sur l’environnement et le climat est tangible tandis que la nature retrouve (un peu) ses droits (ressources naturelles). Or, dès avant l’apparition de la pandémie, un large consensus s’était dégagé au sein du monde politique et économique ainsi que dans la population des pays européens sur la nécessité de réagir énergiquement face à la crise climatique de plus en plus manifeste, qui menace l’humanité à l’horizon d’une génération. Aussi, a fortiori, le redémarrage de l’économie mondiale au lendemain d’un lock-down sanitaire ne peut-il faire l’impasse sur la prise en compte du changement climatique et de ses conséquences sociales, potentiellement beaucoup plus graves encore que celles causées par la pandémie elle-même. Face à un défi aussi colossal, les Académies ne peuvent se contenter de poursuivre quiètement leurs activités comme par le passé. La classe Technologie et société de l’Académie royale de Belgique, en particulier, se doit d’apporter une contribution, même modeste, à la réflexion générale nécessaire à l’éveil de la conscience des citoyens et dont nos dirigeants devraient pouvoir se nourrir afin de prendre les décisions qui s’imposent. Cette problématique, dont il serait naïf de ré- duire le niveau de grande complexité, est promue à un intérêt croissant, car au cœur d’un développement harmonieux des sociétés. Si l’innovation technologique ne peut seule prétendre résoudre les problèmes auxquels le monde est confronté, elle devrait en effet être en mesure d’apporter des réponses efficaces qui, sans cesse, seront pourtant confrontées à la question de la transparence des procédures de recherche et à la publicité des résultats. Dans ce débat, l’Union européenne et ses États membres, dans leurs champs d’action respectifs, auront à intervenir et à se choisir une ligne de conduite, responsable et soucieuse du bien commun.&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">{https://www.societe-informatique-de-france.fr/wp-content/uploads/2021/04/1024_17_2021_155.pdf</style></URL><title><style face="normal" font="default" size="100%">Réflexions sur la diffusion des connaissances a travers les grands colloques internationaux, les revues scientifiques, et la communication libre et rapide entre chercheurs et innovateurs dans un contexte de reduction de l'empreinte climatique</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Mert Nakip</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Randomization of Data Generation Times Improves Performance of Predictive IoT Networks</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">7th IEEE World Forum on the Internet of Things</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">New Orleans, Louisiana, USA</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Input traffic from Internet of Things (IoT) devices is often both periodic and requires to be received by a given deadline. This can create congestion at instants of time when traffic flowing from multiple devices arrives at a shared input port or gateway, resulting in missed deadlines at the receiver. As a consequence, scheduling techniques such as the “Earliest Deadline First” (EDF) and “Priority based on Average Load” (PAL) are used to schedule the flow from different devices so as to try to satisfy the needs of the largest number of traffic flows in a timely fashion. In this paper, we propose the Randomization of flow Genaration Times (RGT) in order to smooth the total incoming traffic to the input port or gateway, on top of the use of EDF and PAL. We then evaluate the performance of RGT together with PAL and EDP, for traffic load with a varying number of up to 6400 IoT devices. Our simulation results show that RGT provides significantly better performance when added to EDF and PAL. Also, the additional computation required by RGT at each device can be quite small, suggesting that RGT is a very useful addition for improving the performance of IoT networks.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://wfiot2021.iot.ieee.org</style></URL><title><style face="normal" font="default" size="100%">Randomization of Data Generation Times Improves Performance of Predictive IoT Networks</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Renforcer la place des universités européennes dans le concert mondial</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Revue des Electriciens et Electroniciens</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">2</style></VOLUME><SECTION><style face="normal" font="default" size="100%">102-107</style></SECTION><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Les pays de l’UE peuvent se targuer de posséder quelques-unes des plus prestigieuses universités et organismes de recherche dans le monde. Pourtant la part des innovations technologiques que produit le continent reste faible au regard du potentiel de recherches que l’UE représente. Comment redresser la barre et mieux armer les universités européennes dans la compétition exacerbée que se livrent les grandes puissances ? Après une analyse du financement et du mode de fonctionnement comparés des universités dans le monde occidental, l’auteur propose quelques recommandations fortes pour donner à la France et l’Europe de la recherche les moyens de se confronter à armes égales avec ses concurrents.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Renforcer la place des universités européennes dans le concert mondial</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Nan Li</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Xiping Hu</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Edith Ngai</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Cooperative Wireless Edges with Composite Resource Allocation in Hierarchical Networks</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%"/></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;With the expansion of the IoT, it is important to optimize available bandwidth to reliably support edge to device communications. Thus we propose a wireless network where each edge server communicates with its end devices using its wireless band as a primary channel, assisted by a secondary edge server that can relay communications via its own wireless band as a secondary channel. The network can optimize capacity by balancing load between primary and secondary wireless bands, and we analyze the geometry of achievable rate regions, depending on the state of bands modeled as Rayleigh fading channels. The allocation of a connection to the primary or secondary band is formulated as an optimization problem which is then solved, and illustrated with numerical examples. Index Terms—Edge computing, cooperation, non-orthogonal multiple access, resource allocation&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Cooperative Wireless Edges with Composite Resource Allocation in Hierarchical Networks</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Piotr Fröhlich</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jerzy Fiołka</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jacek Chęciński</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mateusz Nowak</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Zdzisław Filus</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Smart SDN Management of Fog Services to Optimize QoS and Energy</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Sensors</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">21(9)</style></VOLUME><SECTION><style face="normal" font="default" size="100%">3105</style></SECTION><DATE><style face="normal" font="default" size="100%">April 2021</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">The short latency required by IoT devices that need to access specific services have led to the development of Fog architectures that can serve as a useful intermediary between IoT systems and the Cloud. However, the massive numbers of IoT devices that are being deployed raise concerns about the power consumption of such systems as the number of IoT devices and Fog servers increase. Thus, in this paper, we describe a software-defined network (SDN)-based control scheme for client–server interaction that constantly measures ongoing client–server response times and estimates network power consumption, in order to select connection paths that minimize a composite goal function, including both QoS and power consumption. The approach using reinforcement learning with neural networks has been implemented in a test-bed and is detailed in this paper. Experiments are presented that show the effectiveness of our proposed system in the presence of a time-varying workload of client-to-service requests, resulting in a reduction of power consumption of approximately 15% for an average response time increase of under 2%.</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://www.mdpi.com/1424-8220/21/9/3105</style></URL><title><style face="normal" font="default" size="100%">Smart SDN Management of Fog Services to Optimize QoS and Energy</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>7</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Dan Larhammar</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Maria Baghramian</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piero Bianucci</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Garvin Brod</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dalibor Dobiáš</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Risto Kunelius</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Stefano Leonardi</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Stephan Lewandowsky</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mariëtte Samenweten</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Alison Powell</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jane Suiter</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Fact or Fake? Tackling Science Disinformation</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">ALLEA General Assembly Paper</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Berlin</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">ALLEA - All European Academies</style></PUBLISHER><PAGES><style face="normal" font="default" size="100%">paper 5</style></PAGES><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;The information landscape has undergone dramatic changes with the expansion of the internet and digital social media platforms. Information can be spread more rapidly and can reach more people than ever before. While this offers excellent possibilities to teach and educate and to disseminate information about research results and scientific evidence, it also comes with a downside: False information can be propagated with equal ease and speed. This discussion paper describes and discusses the problems and the consequences of science disinformation in three areas of concern, namely climate change, vaccines and pandemics, and what we can do to increase awareness and minimize harm caused by the spread of disinformation. It does so by highlighting the societal value of the scientific method, research integrity, open science communication and the resulting trust in science. The underlying question is how to protect the pillars of science from the severe consequences of disinformation while maintaining openness and democratic principles. This paper presents the central characteristics of science disinformation, its roots, its spread, and potential solutions. The mere existence of disinformation is hard to prevent in open societies with strong protection of individual rights and freedom of expression. The paper identifies underlying cognitive, social and economic mechanisms that amplify the spread of disinformation and evaluates potential solutions. Extensive research over the past several years has identified cognitive features of the human mind, as well as fast and efficient transmission channels, that contribute to the prevalence of science disinformation in our societies. Potential solutions cover a range of psychological, technical and political measures including inoculation, debunking, recommender systems, fact-checking, raising awareness, media literacy, and innovations in science communication and public engagement. Together, they contribute to tackling problems such as knowledge resistance, pseudoscience, undermining of trust, confirmation bias, filter bubbles, echo chambers, and other problems related to science disinformation.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Fact or Fake? Tackling Science Disinformation</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>9</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Sabu Thampi</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mohammed Atiquzzaman</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Vipin Chaudhary</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Kuan-Ching Li</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Advances in Computing and Network Communications</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2020</style></YEAR><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><VOLUME><style face="normal" font="default" size="100%">1</style></VOLUME><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;The Fourth International Conference on Computing and Network Communications (CoCoNet’20) provided a forum for sharing original research outcomes and practical development experiences among experts in the emerging areas of computing and communications. The conference was organized by Vellore Institute of Technology (VIT), Chennai, India. Due to the recent pandemic situation, the conference was conducted as a virtual event during October 14–17, 2020. The material was presented in a program that consisted of keynote talks, technical sessions, lightning talks, tutorials, symposiums, parallel sessions, workshops and hot off the press. CoCoNet’20 convened a well-tailored and handpicked collection of eminent speakers from renowned universities and industries in and outside India. The array of speakers included Dr. Ian T. Foster, Dr. Arumugam Nallanathan, Dr. Mohammed Atiquzzaman, Dr. Vipin Chaudhary, Dr. Dilip Krishnaswamy and Dr. Ljiljana Trajkovic. The conference received 181 submissions this year, out of which 98 papers (58 regular papers and 40 short papers) had been accepted. The papers were subjected to a rigorous review process that examined the significance, novelty and technical quality of the submission. The papers were presented in different sessions, namely the best paper sessions, regular paper sessions and short paper sessions. The proceedings of the conference is organized into two volumes. This volume is comprised of 51 papers, and the topical sections include Communications, Control and Signal Processing, Data Analytics and Networked Systems and Security. The success of the conference depends ultimately on the numerous people who have worked with us to plan and organize both the technical program and local arrangements. In particular, the wise advice and brilliant ideas of the program chairs, workshop and symposium chairs and industry track chairs in the organization of the technical program are gratefully appreciated. We would like to extend our heartfelt gratitude to the organizing committee and advisory committee members. The accomplishment of the motives of the conference is powered by the tireless efforts of many individuals. We would like to express our sincere gratitude to the TPC chairs, TPC members and additional reviewers who shared their technical expertise and assisted us in reviewing all the submitted papers. We would like to thank the general chairs, organizing committee members, steering committee, keynote speakers, session chairs and the conference attendees. We are thankful to&#160;all the authors for choosing this conference as a venue for presenting their research works. We express our wholehearted appreciation to the contributions of all those who apportioned their valuable time for the success of CoCoNet’20. We are grateful to Vellore Institute of Technology (VIT), Chennai, for organizing the conference. Recognition should go to the local organizing committee members who all have worked extremely hard for the details of important aspects of the conference programs. We appreciate the contributions of all the faculty and staff of VIT and the student volunteers who have selflessly contributed their time to make this virtual conference successful. We would like to express our gratitude to Senior Editor of Springer Nature, Aninda Bose, for his help and cooperation. We sincerely hope that CoCoNet’20 turned out to be a forum for excellent discussions that enabled new ideas to come about, promoting collaborative research. We are confident that the proceedings will serve as a momentous source of research references and knowledge, which will lead not only to the scientific and engineering findings but also to the development of new products and technologies.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Advances in Computing and Network Communications</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Guy Brasseur</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Luc Chefneux</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Véronique Devant</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Véronique Halloin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jean-Paul Haton</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Michel Judkiewicz</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Bernard Rentier</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Romain Weikmans</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">On	Sharing	Knowledge	And	Fostering	 “Open	Science”</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Ubiquity (ACM Journal)</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">2021</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">5</style></ISSUE><SECTION><style face="normal" font="default" size="100%">1-13</style></SECTION><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;The crucial importance of science and technology and its accurate peer reviewed dissemination, has once again been demonstrated during the current pandemic. Thus the COVID-19 pandemic together with the inevitable energy transition required by climate change, lead us to consider the issue of scientific and technical communication, both for the written papers and proceedings that have largely moved online (but not always in open access), and the various types of seminars, workshops, and symposia that frequently involve air travel with substantial CO2 impact. Online meetings that have become recently very popular, as well as online repositories for publications, themselves have a significant CO2—as well as environmental—impact, due to the massive use of electricity by information and communication technologies (ICT) and of the environmentally unfriendly manufacturing processes and decommissioning of ICT equipment. Presented is a broad overview of these aspects, and some recommendations regarding the future organization of scientific and technical communication, including: (1) peer-reviewed journals and proceedings with online open access; (2) the importance of face to face seminars and symposia, together with online meetings, to maintain the serendipity and importance of direct human contact while reducing the need for air travel; (3) the peer evaluation of research and of academic and research staff and its dependence on publications and their qualitative—rather than excessively quantitative—evaluation, where the concept of impact should include the usefulness of research to education, industry and society; (4) and the crucial role of ICT in all these aspects and the questions raised by the sustainability of ICT itself.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">On	Sharing	Knowledge	And	Fostering	 “Open	Science”</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Miltiadis Siavvas</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dionysios Kehagias</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dimitrios Tzovaras</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">A hierarchical model for quantifying software security based on static analysis alerts and software metrics</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Software Quality Journal</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">29</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">2</style></ISSUE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Despite the acknowledged importance of quantitative security assessment in secure software development, current literature still lacks an efcient model for measuring internal software security risk. To this end, in this paper, we introduce a hierarchical security assessment model (SAM), able to assess the internal security level of software products based on low-level indicators, i.e., security-relevant static analysis alerts and software metrics. The model, following the guidelines of ISO/IEC 25010, and based on a set of thresholds and weights, systematically aggregates these low-level indicators in order to produce a high-level security score that refects the internal security level of the analyzed software. The proposed model is practical, since it is fully automated and operationalized in the form of a standalone tool and as part of a broader Computer-Aided Software Engineering (CASE) platform. In order to enhance its reliability, the thresholds of the model were calibrated based on a repository of 100 popular software applications retrieved from Maven Repository. Furthermore, its weights were elicited in a way to chiefy refect the knowledge expressed by the Common Weakness Enumeration (CWE), through a novel weights elicitation approach grounded on popular decision-making techniques. The proposed model was evaluated on a large repository of 150 open-source software applications retrieved from GitHub and 1200 classes retrieved from the OWASP Benchmark. The results of the experiments revealed the capacity of the proposed model to reliably assess internal security at both product level and class level of granularity, with sufcient discretion power. They also provide preliminary evidence for the ability of the model to be used as the basis for vulnerability prediction. To the best of our knowledge, this is the frst fully automated, operationalized and sufciently evaluated security assessment model in the modern literature&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">A hierarchical model for quantifying software security based on static analysis alerts and software metrics</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Piotr Fröhlich</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mateusz Nowak</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Reinforcement Learning and Energy-Aware Routing</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">ACM 4th FlexNets'21 Workshop, ACM SIGCOMM 2021</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">ACM</style></PUBLISHER><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;We present an approach that uses Reinforcement Learning (RL) with the Random Neural Network (RNN) acting as an adaptive critic, to route traffic in a SDN network, so as to minimize a composite Goal function that includes both packet delay and energy consumption per packet. We directly measure the traffic dependent energy consumption characeristics of the hardware that we use (including energy expended per packet) so as to parametrize the Goal function. The RL based algorithm with the RNN is implemented in a SDN controller that manages a multi-hop network which assigns service requests to specific servers so as to minimize the desired Goal. The overall system’s performance is evaluated through experimental measurements of packet delay and energy consumption under different traffic load values, demonstrating the effectiveness of the proposed approach.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://dl.acm.org/doi/pdf/10.1145/3472735.3473390</style></URL><title><style face="normal" font="default" size="100%">Reinforcement Learning and Energy-Aware Routing</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Mert Nakip</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">MIRAI Botnet Attack Detection with Auto-Associative Dense Random Neural Network</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">2021 IEEE Global Communications Conference</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Barcelona</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">7-11 Dec 2021</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">Internet connected IoT devices have often been particularly vulnerable to Botnet attacks of the Mirai family in recent years. Thus we develop an attack detection scheme for Mirai Botnets, using the Auto-Associative Dense Random Neural Network that has recently been successful for other attacks such as the SYN attack. The resulting method is trained with normal traffic and tested with attack traffic, and shown to result in high accuracy detection of attacks with low false alarms. The approach is compared on the same data set with two other common Machine learning methods (Lasso and KNN) and shown to have higher accuracy, and much lower computation times than KNN and slightly higher (but comparable) computation times with respect to Lasso.</style></ABSTRACT><title><style face="normal" font="default" size="100%">MIRAI Botnet Attack Detection with Auto-Associative Dense Random Neural Network</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mert Nakip</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dariusz Marek</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Diffusion Analysis Improves Scalability of IoTNetworks to Mitigate the Massive Access Problem</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">2021 Mascots: 29th International Symposium on the Modelling, Analysis and Simulation of Computer and Telecommunication Systems</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Houston, Texas, USA</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">11/2021</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;A significant challenge of IoT networks is to offer Quality of Service (QoS) and meet deadline requirements when packets from a massive number of IoT devices are forwarded to an IoT gateway. Many IoT devices tend to report their data to their wired or wireless network gateways at closely correlated instants of time, leading to congestion known as the MassiveAccess Problem (MAP), which increases the probability that the IoT data will not meet its required deadlines. Since IoT data loses much of its value if it arrives to destination beyond a required deadline, MAP has been extensively studied in the literature. Thus we first take a queueing theoretic view of the problem, and also use a Diffusion Approximation to gain insight into the IoT traffic statistics that affect MAP. Then we introduce the Quasi-Deterministic Transmission Policy (QDTP) which significantly alleviates MAP when the average traffic rate grows beyond a given level and substantially reduces the probability that IoT data deadlines are missed. The results are validated using real IoT data which has been placed in IP packets for transmission.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Diffusion Analysis Improves Scalability of IoTNetworks to Mitigate the Massive Access Problem</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">NOBEL PRIZE 50 YEARS AGO</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Europhysics News </style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">52</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">5</style></ISSUE><SECTION><style face="normal" font="default" size="100%">40-42</style></SECTION><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;As we recall the major contributions of Professor Dennis Gabor that resulted in his Nobel Prize in Physics in 1971 for the invention of holography, it is interesting to put his impact on science, technology, as well as humanity, in a broader context so as to better understand his experiences, and recognize the very significant role that he played in his time.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">NOBEL PRIZE 50 YEARS AGO</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Charalampos Marantos</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Miltiadis Siavvas</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dimitrios Tsoukalas</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Christos Lamprakos</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Lazaros Papadopoulos</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Paweł Boryszko</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Katarzyna Filus</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Joanna Domańska</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Apostolos Ampatzoglou</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Alexander Chatzigeorgiou</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dionysios Kehagias</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dimitrios Soudris</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">SDK4ED: One-click platform for Energy-aware, Maintainable and Dependable Applications</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2022</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">25th Design, Automation and Test in Europe Conference</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Belgium</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><DATE><style face="normal" font="default" size="100%">03/2022</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;Developing&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;modern&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;secure&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;and&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;low-energy&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;applica&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;tions in a short time imposes new challenges and creates the need &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;of&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;designing&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;new&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;&#160;&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;software&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;tools&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;to&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;assist&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;developers&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;in&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;all&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;phases &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;of&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;application&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;development.&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;The&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;design&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;of&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;such&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;tools&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;cannot &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;be&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;considered&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;a&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;trivial&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;task,&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;as&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;they&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;should&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;be&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;able&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;to&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;provide&lt;/span&gt; &lt;span style="font-family:sans-serif; font-size:14.944px"&gt;optimization&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;of&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;multiple&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;quality&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;requirements.&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;In&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;this&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;paper,&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;we &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;introduce&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;the&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;SDK4ED&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;platform,&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;which&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;incorporates&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;advanced&lt;/span&gt; &lt;span style="font-family:sans-serif; font-size:14.944px"&gt;methods and tools for measuring and optimizing maintainability, &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;dependability&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;and&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;energy.&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;The&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;presented&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;solution&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;offers&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;a&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;com&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;plete&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;tool-flow&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;for&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;providing&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;indicators&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;and&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;optimization&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;meth&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;ods&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;with&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;emphasis&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;on&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;embedded&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;software.&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;Effective&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;forecasting&lt;/span&gt; &lt;span style="font-family:sans-serif; font-size:14.944px"&gt;models&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;and&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;decision-making&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;solutions&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;are&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;also&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;implemented&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;to &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;improve&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;the&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;quality&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;of&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;the&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;software,&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;respecting&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;the&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;constraints&lt;/span&gt; &lt;span style="font-family:sans-serif; font-size:14.944px"&gt;imposed&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;on&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;maintenance&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;standards,&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;energy&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;consumption&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;limits &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;and&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;security&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;vulnerabilities.&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;The&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;use&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;of&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;the&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;SDK4ED&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;platform&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;is&lt;/span&gt; &lt;span style="font-family:sans-serif; font-size:14.944px"&gt;demonstrated&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;in&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;a&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;healthcare&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;embedded&lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt; &lt;/span&gt;&lt;span style="font-family:sans-serif; font-size:14.944px"&gt;application.&lt;/span&gt;&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">SDK4ED: One-click platform for Energy-aware, Maintainable and Dependable Applications</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Aleksander Byrski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Krzysztof Grochla</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Yuko Murayama</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Computer Science Protecting Human Society Against Epidemics</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">ANTICOVID 2021</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Virtual Event, June 28–29, 2021</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><VOLUME><style face="normal" font="default" size="100%">616</style></VOLUME><NUMBER><style face="normal" font="default" size="100%">616</style></NUMBER><PAGES><style face="normal" font="default" size="100%">125</style></PAGES><TERTIARY_TITLE><style face="normal" font="default" size="100%">IFIP Advances in Information and Communication Technology</style></TERTIARY_TITLE><ISBN><style face="normal" font="default" size="100%">978-3-030-86582-5</style></ISBN><title><style face="normal" font="default" size="100%">Computer Science Protecting Human Society Against Epidemics</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mert Nakip</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dariusz Marek</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Mitigating the Massive Access Problem in the Internet of Things</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2022</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%"> EuroCybersec</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Nice, France</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;The traffic from the large number of IoT devices connected to the IoT is a source of congestion known as the Massive Access Problem (MAP), that results in packet losses, delays and missed deadlines for realtime data. This paper reviews the literature on MAP and summarizes recent results on two approaches that have been designed to mitigate MAP. One approach is based on randomizing the packet arrival instants to IoT gateways within a given time interval that is chosen so that packet arrivals do not exceed their deadlines, but also so that they do not constitute bulk arrivals. The second approach is a novel traffic shaping policy named the Quasi-Deterministic-Transmission-Policy (QDTP) which has been proved to drastically reduce queue formation at the receiving gateway by delaying packet departures from the IoT devices in a judicious manner. Both analytical and experimental results are summarized, that describe the benefits of these techniques.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Mitigating the Massive Access Problem in the Internet of Things</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Mert Nakip</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Botnet Attack Detection with Incremental Online Learning</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2022</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">EuroCybersec 2021</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Nice, France</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;In recent years, IoT devices have often been the target of Mirai Botnet attacks. This paper develops an intrusion detection method based on Auto-Associated Dense Random Neural Network with incremental online learning, targeting the detection of Mirai Botnet attacks. The proposed method is trained only on benign IoT traffic while the IoT network is online; therefore, it does not require any data collection on benign or attack traffic. Experimental results on a publicly available dataset have shown that the performance of this method is considerably high and very close to that of the same neural network model with offline training. In addition, both the training and execution times of the proposed method are highly acceptable for real-time attack detection.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Botnet Attack Detection with Incremental Online Learning</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Miltiadis Siavvas</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dimitrios Tsoukalas</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ilias Kalouptsoglou</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Maria Mathioudaki</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mert Nakip</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dionysios Kehagias</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dimitrios Tzovaras</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">The IoTAC Software Security-by-Design Platform: Concept, Challenges, and Preliminary Overview</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2022</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">18th International Conference on the Design of Reliable Communication Networks (DRCN)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Vilanova i la Geltrú, Spain</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Critical everyday activities handled by modern IoT Systems imply that security is of major concern both for the end-users and the industry. Securing the IoT System Architecture is commonly used to strengthen its resilience to malicious attacks. However, the security of software running on the IoT must be considered as well, since the exploitation of its vulnerabilities can infringe the security of the overall system, regardless of how secure its architecture may be. Thus, we present an IoT Software Security-by-Design (SSD) Platform, which provides mechanisms for monitoring and optimizing the security of IoT software applications throughout their development lifecycle, to validate the broader security of the IoT software. This paper describes the proposed SSD platform that leverages security information from all phases of development, using some novel mechanisms that have been implemented, and which can lead to a holistic security evaluation and future security certification.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">The IoTAC Software Security-by-Design Platform: Concept, Challenges, and Preliminary Overview</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mert Nakip</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Improving Massive Access to IoT Gateways</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2022</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Performance Evaluation</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><SECTION><style face="normal" font="default" size="100%">102308</style></SECTION><DATE><style face="normal" font="default" size="100%">08/2022</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;IoT networks handle incoming packets from large numbers of IoT Devices (IoTDs) to IoT Gateways. This can lead to the IoT Massive Access Problem that causes buffer overflow, large end-to-end delays and missed deadlines. This paper analyzes a novel traffic shaping method named the Quasi-Deterministic Traffic Policy (QDTP) that mitigates this problem by shaping the incoming traffic without increasing the end-to-end delay or dropping packets. Using queueing theoretic techniques and extensive data driven simulations with real IoT datasets, the value of QDTP is shown as a means to considerably reduce congestion at the Gateway, and significantly improve the IoT network’s overall performance.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Improving Massive Access to IoT Gateways</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mert Nakip</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">G-Networks Can Detect Different Types of Cyberattacks</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2022</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">2022 Mascots: 30th International Symposium on the Modelling, Analysis and Simulation of Computer and Telecommunication Systems</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Nice, France</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Malicious network attacks are a serious source of concern, and machine learning techniques are widely used to build Attack Detectors with off-line training with real attack and non-attack data, and used online to monitor system entry points connected to networks. Many machine learning based Attack Detectors are typically trained to identify specific types attacks, and the training of such algorithms to cover several types of attacks may be excessively time consuming. This paper shows that G-Networks, which are queueing networks with product form solution and special customers such as negative customers and triggers, can be trained just with “non-attack” traffic, can accurately detect several different attack types. This is established with a special case of G-Networks with triggerred customer movement. A DARPA attack and non-attack traffic repository is used to train and test the the G-Network, yielding comparable or clearly better accuracy than most known attack detection techniques.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">G-Networks Can Detect Different Types of Cyberattacks</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Karl Sigman</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">IoT Traffic Shaping and the Massive Access Problem</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2022</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">ICC 2022, IEEE International Conference on Communications, 16–20 May 2022, Seoul, South Korea</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Seoul, South Korea </style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE Xplore, CC 2022 - IEEE International Conference on Communications, 2022, pp. 2732-2737</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">08/2022</style></DATE><ISBN><style face="normal" font="default" size="100%">978-1-5386-8347-7</style></ISBN><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;IoT gateways aim to meet the deadlines and QoS&#160;needs of packets from as many IoT devices as possible, though this can lead to a form of congestion known as the Massive&#160;Access Problem (MAP). While much work was conducted on&#160;predictive or reactive scheduling schemes to match the arrival&#160;process of packets to the service capabilities of IoT gateways, such&#160;schemes may use substantial computation and communication&#160;between gateways and IoT devices. This paper proves that&#160;the recently proposed “Quasi-Deterministic-Transmission-Policy&#160;(QDTP)” traffic shaping approach which delays packets at IoT&#160;devices, substantially alleviates the MAP: QDTP does not increase&#160;overall end-to-end delay and reduces gateway queue length. We&#160;then introduce the Adaptive Non-Deterministic Transmission Policy (ANTP) that requires only one packet buffer at the gateway,&#160;offering substantial QoS improvement over FIFO scheduling.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">IoT Traffic Shaping and the Massive Access Problem</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Huibo Bi</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Searching and Rescuing Victims in Emergency: A Comprehensive Survey</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2022</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">CECNet 2021</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IOS Press</style></PUBLISHER><title><style face="normal" font="default" size="100%">Searching and Rescuing Victims in Emergency: A Comprehensive Survey</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Marija Jankovic</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dionysios Kehagias</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Anna Marton</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Andras Vilmos</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Security in Computer and Information Sciences: Second International Symposium</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2022</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">EuroCybersec 2021</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer Nature</style></PUBLISHER><title><style face="normal" font="default" size="100%">Security in Computer and Information Sciences: Second International Symposium</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mateusz Nowak</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piotr Fröhlich</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jerzy Fiolka</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jacek Chęciński</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Energy, QoS and Security Aware Edge Services</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2022</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">International ISCIS Security Workshop</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><title><style face="normal" font="default" size="100%">Energy, QoS and Security Aware Edge Services</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>10</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Guy Brasseur</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Luc Cheffneux</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Véronique Dehant</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Anna Fabjanska</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Véronique Halloin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Michel Judkiewicz</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Vladimir Mrsa</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ignacio Llorente</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Challenges for European Science and Technology Driven Innovation in Europe</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2022</style></YEAR><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><URL><style face="normal" font="default" size="100%">https://www.euro-case.org/challenges-for-european-science-and-technology-driven-innovation-in-europe/</style></URL><title><style face="normal" font="default" size="100%">Challenges for European Science and Technology Driven Innovation in Europe</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Godlove Kuaban</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dariusz Marek</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Optimizing Energy Usage for an Electric Drone</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2022</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">EuroCybersec 2021</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Nice, France</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><VOLUME><style face="normal" font="default" size="100%">1596</style></VOLUME><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Unmanned Aerial Vehicles (UAVs) are rapidly gaining popu-&lt;br /&gt;&#13;
larity in a wide variety of applications, e.g., agriculture, health care, envi-&lt;br /&gt;&#13;
ronmental management, supply chains, law enforcement, surveillance,&lt;br /&gt;&#13;
and photography. Dones are often powered by batteries, making energy&lt;br /&gt;&#13;
a critical resource that must be optimised during the mission of the&lt;br /&gt;&#13;
drone. The duration of a done’s mission depends on the amount of energy&lt;br /&gt;&#13;
required to perform some manoeuvering actions (takeoff, level flight, hov-&lt;br /&gt;&#13;
ering, and landing), the energy required to power the ICT modules in&lt;br /&gt;&#13;
the drone, the drone’s speed, payload, and the wind. In this paper, we&lt;br /&gt;&#13;
present a model that minimizes the energy consumption of a low power&lt;br /&gt;&#13;
drone and maximizes the time required to completely drain the drone’s&lt;br /&gt;&#13;
battery and ensure the safe landing of the drone.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Optimizing Energy Usage for an Electric Drone</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Godlove Kuaban</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Modelling energy changes in the energy harvesting battery of an IoT device</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2022</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">30th International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2022)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Nice, France</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;The complexity of battery-powered autonomous de-&lt;br /&gt;&#13;
vices such as Internet of Things (IoT) nodes or Unmanned Aerial&lt;br /&gt;&#13;
Vehicles (UAV) and the necessity to ensure an acceptable quality&lt;br /&gt;&#13;
of service, reliability, and security, have significantly increased&lt;br /&gt;&#13;
their energy demand. In this paper, we discuss using a diffusion&lt;br /&gt;&#13;
approximation process to approximate the dynamic changes in&lt;br /&gt;&#13;
the energy content of a battery. We consider the case when&lt;br /&gt;&#13;
energy harvesting sources are constantly charging the battery.&lt;br /&gt;&#13;
The model assumes a probabilistic consumption and delivery of&lt;br /&gt;&#13;
energy, giving the time-dependent distributions of the energy at&lt;br /&gt;&#13;
the battery, of the time remaining until it becomes empty, the&lt;br /&gt;&#13;
time required to charge the battery to its total capacity, or the&lt;br /&gt;&#13;
time it is operational between two moments of complete depletion.&lt;br /&gt;&#13;
When possible, we compare the diffusion approximation results&lt;br /&gt;&#13;
with corresponding Markovian models.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Modelling energy changes in the energy harvesting battery of an IoT device</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mateusz Nowak</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piotr Fröhlich</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jerzy Fiolka</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jacek Chęciński</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Energy, QoS and Security Aware Edge Services</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2022</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">EuroCybersec 2021</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;With the development of communication technologies and the increasing bandwidth of optical fibres and transmission speeds in current 5G and future 6G wireless networks, there is a growing demand for solutions organising traffic in such networks, taking into account both end-to-end transmissions and the possibility of data processing by edge services. The most pressing problems of today’s computer networks are not only bandwidth and transmission delays, but also security and energy consumption, which is becoming increasingly important in today’s climate. This paper presents a solution based on neural networks that organises network traffic taking into account the above criteria - quality of service (QoS), energy consumption and security.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Energy, QoS and Security Aware Edge Services</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">NOBEL PRIZE 50 YEARS AGO</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2022</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">EPN</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">52</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">5</style></ISSUE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;As we recall the major contributions of Professor Dennis Gabor that resulted in his&lt;br /&gt;&#13;
Nobel Prize in Physics in 1971 for the invention of holography, it is interesting to put&lt;br /&gt;&#13;
his impact on science, technology, as well as humanity, in a broader context so as&lt;br /&gt;&#13;
to better understand his experiences, and recognize the very significant role that he&lt;br /&gt;&#13;
played in his time.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">NOBEL PRIZE 50 YEARS AGO</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Paul Pearce</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">V. Kaptan</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Yu Wang</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">N.S Walmsley</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">P. Gardiner</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">J. Moffat</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">A Dynamic Model for Identifying Enemy Collective Behaviour</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2022</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">11th ICCRTS Coalition Command and Control in the Networked Era</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Recent advances in Command and Control (C2) modelling have developed&lt;br /&gt;&#13;
algorithmic representations of the command decision making process at both&lt;br /&gt;&#13;
the tactical (Rapid Planning) and operational (Deliberate Planning) levels of&lt;br /&gt;&#13;
command. In this paper, the development of methods by which indicators of&lt;br /&gt;&#13;
enemy/group behaviour can be extracted is discussed. The aim of this work&lt;br /&gt;&#13;
is to use the grouping techniques to establish, with more certainty, force size&lt;br /&gt;&#13;
and direction. We describe a multi-agent model approach, based on a&lt;br /&gt;&#13;
hierarchical framework, suitable for identifying indicators of collaborative&lt;br /&gt;&#13;
behaviour associated with enemy agile mission groups that commonly feature&lt;br /&gt;&#13;
in the modern Networked Era.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">A Dynamic Model for Identifying Enemy Collective Behaviour</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Debanjan Konar</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Soham Bhandary</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Aditya Sarma</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Attila Cangi</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Random Quantum Neural Networks (RQNN) for Noisy Image Recog- nition</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2022</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE Xplore</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Classical Random Neural Networks (RNNs) have demonstrated effective applications in decision&lt;br /&gt;&#13;
making, signal processing, and image recognition tasks. However, their implementation has been&lt;br /&gt;&#13;
limited to deterministic digital systems that output probability distributions in lieu of stochastic be-&lt;br /&gt;&#13;
haviors of random spiking signals. We introduce the novel class of supervised Random Quantum&lt;br /&gt;&#13;
Neural Networks (RQNNs) with a robust training strategy to better exploit the random nature of&lt;br /&gt;&#13;
the spiking RNN. The proposed RQNN employs hybrid classical-quantum algorithms with super-&lt;br /&gt;&#13;
position state and amplitude encoding features, inspired by quantum information theory and the&lt;br /&gt;&#13;
brain’s spatial-temporal stochastic spiking property of neuron information encoding. We have ex-&lt;br /&gt;&#13;
tensively validated our proposed RQNN model, relying on hybrid classical-quantum algorithms via&lt;br /&gt;&#13;
the PennyLane Quantum simulator with a limited number of qubits. Experiments on the MNIST,&lt;br /&gt;&#13;
FashionMNIST, and KMNIST datasets demonstrate that the proposed RQNN model achieves an av-&lt;br /&gt;&#13;
erage classification accuracy of 94.9%. Additionally, the experimental findings illustrate the proposed&lt;br /&gt;&#13;
RQNN’s effectiveness and resilience in noisy settings, with enhanced image classification accuracy&lt;br /&gt;&#13;
when compared to the classical counterparts (RNNs), classical Spiking Neural Networks (SNNs), and&lt;br /&gt;&#13;
the classical convolutional neural network (AlexNet). Furthermore, the RQNN can deal with noise,&lt;br /&gt;&#13;
which is useful for various applications, including computer vision in NISQ devices. The PyTorch&lt;br /&gt;&#13;
code 1 is made available on GitHub to reproduce the results reported in this manuscript.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Random Quantum Neural Networks (RQNN) for Noisy Image Recog- nition</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jankovic Marija</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Kehagias Dionysios</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Marton Anna</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Vilmos Andras</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Security in Computer and Information Sciences</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2022</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Second International Symposium, EuroCybersec 2021</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Nice, France</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><title><style face="normal" font="default" size="100%">Security in Computer and Information Sciences</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mert Nakip</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Traffic Based Sequential Learning During Botnet Attacks to Identify Compromised IoT Devices</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2022</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE Access</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">10</style></VOLUME><SECTION><style face="normal" font="default" size="100%">126536</style></SECTION><DATE><style face="normal" font="default" size="100%">10/2022</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;A novel online Compromised Device Identification System (CDIS) is presented to identify IoT devices and/or IP addresses that are compromised by a Botnet attack, within a set of sources and destinations that transmit packets. The method uses specific metrics that are selected for this purpose and which are easily extracted from network traffic, and trains itself online during normal operation with an Auto-Associative Dense Random Neural Network (AADRNN) using traffic metrics measured as traffic arrives. As it operates, the AADRNN is trained with auto-associative learning only using traffic that it estimates as being benign, without prior collection of different attack data. The experimental evaluation on publicly available Mirai Botnet attack data shows that CDIS achieves high performance with Balanced Accuracy of 97%, despite its low on-line training and execution time. Experimental comparisons show that the AADRNN with sequential (online) auto-associative learning, provides the best performance among six different state-of-the-art machine learning models. Thus CDIS can provide crucial effective information to prevent the spread of Botnet attacks in IoT networks having multiple devices and IP addresses.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/9969594</style></URL><title><style face="normal" font="default" size="100%">Traffic Based Sequential Learning During Botnet Attacks to Identify Compromised IoT Devices</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Godlove Kuaban</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Modelling energy changes in the energy harvesting battery of an IoT device</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2023</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">30th International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2022)  pp. 81-88</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Nice, France</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE Xplore</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">03/2023</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;The complexity of battery-powered autonomous devices such as Internet of Things (IoT) nodes or Unmanned Aerial Vehicles (UAV) and the necessity to ensure an acceptable quality of service, reliability, and security have significantly increased their energy demand. In this paper, we discuss using a diffusion approximation process to approximate the dynamic changes in the energy content of a battery. We consider the case when energy harvesting sources are constantly charging the battery. The model assumes a probabilistic consumption and delivery of energy, giving the time-dependent distributions of the energy at the battery, of the time remaining until it becomes empty, the&lt;br /&gt;&#13;
time required to charge the battery to its total capacity, or the time it is operational between two moments of complete depletion.&lt;br /&gt;&#13;
When possible, we compare the diffusion approximation results with corresponding Markovian models.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Modelling energy changes in the energy harvesting battery of an IoT device</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>7</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Godlove Kuaban</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dariusz Marek</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Optimizing Energy Usage for an Electric Drone</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2022</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">EuroCybersec 2021: Security in Computer and Information Sciences</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Nice, France</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><VOLUME><style face="normal" font="default" size="100%">1596</style></VOLUME><NUMBER><style face="normal" font="default" size="100%">CCIS</style></NUMBER><PAGES><style face="normal" font="default" size="100%">61–75</style></PAGES><ISBN><style face="normal" font="default" size="100%">978-3-031-09357-9</style></ISBN><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Unmanned Aerial Vehicles (UAVs) are rapidly gaining popularity in a wide variety of applications, e.g., agriculture, health care, environmental management, supply chains, law enforcement, surveillance, and photography. Dones are often powered by batteries, making energy&lt;br /&gt;&#13;
a critical resource that must be optimised during the mission of the drone. The duration of a done’s mission depends on the amount of energy required to perform some manoeuvering actions (takeoff, level flight, hovering, and landing), the energy required to power the ICT modules in the drone, the drone’s speed, payload, and the wind. In this paper, we present a model that minimizes the energy consumption of a low-power drone and maximizes the time required to completely drain the drone’s battery and ensure the safe landing of the drone.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Optimizing Energy Usage for an Electric Drone</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mert Nakip</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Real-Time Cyberattack Detection with Offline and Online Learning</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2023</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE International Symposium on Local and Metropolitan Area Networks</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">London, United Kingdom</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;This paper presents several novel algorithms for real-time cyberattack detection using the Auto-Associative Deep Random Neural Network, which were developed in the HORIZON 2020 IoTAC Project. Some of these algorithms require offline learning, while others require the algorithm to learn during its normal operation while it is also testing the flow of incoming traffic to detect possible attacks. Most of the methods we present are designed to be used at a single node, while one specific method collects data from multiple network ports to detect and monitor the spread of a Botnet. The evaluation of the accuracy of all the methods is carried out with real attack traces. These novel methods are also compared with other state-of-the-art approaches, showing that they offer better or equal performance,&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/10189812</style></URL><title><style face="normal" font="default" size="100%">Real-Time Cyberattack Detection with Offline and Online Learning</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Mohammed Nasereddin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mert Nakip</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Measurement Based Evaluation and Mitigation of Flood Attacks on a LAN Test-Bed</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2023</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">The 48th IEEE Conference on Local Computer Networks</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Daytona Beach, Florida, USA</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;The IoT is vulnerable to network attacks, and Intrusion Detection Systems (IDS) can provide high attack detection accuracy and are easily installed in IoT Servers. However, IDS are seldom evaluated in operational conditions which are seriously impaired by attack overload. Thus a Local Area Network testbed is used to evaluate the impact of UDP Flood Attacks on an IoT Server, whose first line of defence is an accurate IDS. We show that attacks overload the multi-core Server and paralyze its IDS. Thus a mitigation scheme that detects attacks rapidly, and drops packets within milli-seconds after the attack begins, is proposed and experimentally evaluated.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/10223358</style></URL><title><style face="normal" font="default" size="100%">Measurement Based Evaluation and Mitigation of Flood Attacks on a LAN Test-Bed</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mohammed Nasereddin</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Protecting IoT Servers Against Flood Attacks with the Quasi Deterministic Transmission Policy</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2023</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">The 22nd IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom-2023)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Exeter, UK</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Servers at Supply Chains that receive packets from IoT devices should meet the QoS needs of incoming packets, and protect the system from Cyberattacks. UDP Floods are often part of Cyberattacks that overwhelm Supply Chains and the IoT through congestion that paralyzes their operation and limits their ability for timely Attack Detection.Thus this paper proposes an architecture that protects a connected Server using a Smart Quasi-Deterministic Transmission Policy Forwarder placed at its input port. This Forwarder shapes the incoming traffic, sends it to the Server without modifying the overall packet delay, and avoids Server congestion. The relevant theoretical background is reviewed, and measurements during a UDP Flood Attack are provided to compare the Server performance, with and without the Forwarder. It is seen that during a UDP Flood Attack, the Forwarder protects the Server from congestion allowing it to effectively identify Attack Packets. The resulting Forwarder congestion is also eliminated with “drop” commands generated by the Forwarder, or sent by the Server to the Forwarder.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/abstract/document/10538961</style></URL><title><style face="normal" font="default" size="100%">Protecting IoT Servers Against Flood Attacks with the Quasi Deterministic Transmission Policy</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Mert Nakip</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Baran Gül</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Modeling, Analysis, and Simulation On Computer and Telecommunication Systems (MASCOTS)</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Cyberattacks are increasingly threatening networked systems, often with the emergence of new types of unknown (zero-day) attacks and the rise of vulnerable devices. While Machine Learning (ML)-based Intrusion Detection Systems (IDSs) have been shown to be extremely promising in detecting these attacks, the need to learn large amounts of labeled data often limits the applicability of ML-based IDSs to cybersystems that only have access to private local data. To address this issue, this paper proposes a novel Decentralized and Online Federated Learning Intrusion Detection (DOF-ID) architecture. DOF-ID is a collaborative learning system that allows each IDS used for a cybersystem to learn from experience gained in other cybersystems in addition to its own local data without violating the data privacy of other systems. As the performance evaluation results using public Kitsune and Bot-IoT datasets show, DOF-ID significantly improves the intrusion detection performance in all collaborating nodes simultaneously with acceptable computation time for online learning.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Mert Nakip</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Online Self-Supervised Deep Learning for Intrusion Detection Systems</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE Transactions on Information Forensics and Security</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">19</style></VOLUME><SECTION><style face="normal" font="default" size="100%">5668</style></SECTION><DATE><style face="normal" font="default" size="100%">05/2024 </style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;This paper proposes a novel Self-Supervised Intrusion Detection (SSID) framework, which enables a fully online {Deep Learning (DL)} based Intrusion Detection System (IDS) that requires no human intervention or prior off-line learning. The proposed framework analyzes and labels incoming traffic packets based only on the decisions of the IDS itself using an Auto-Associative Deep Random Neural Network, and on an online estimate of its statistically measured trustworthiness. The SSID framework enables IDS to adapt rapidly to time-varying characteristics of the network traffic, and eliminates the need for offline data collection. This approach avoids human errors in data labeling, and human labor and computational costs of model training and data collection. The approach is experimentally evaluated on public datasets and compared with well-known {machine learning and deep learning} models, showing that this SSID framework is very useful and advantageous as an accurate and online learning DL-based IDS for IoT systems.&lt;/p&gt;&#13;
&#13;
&lt;p&gt;This paper proposes a novel Self-Supervised Intrusion Detection (SSID) framework, which enables a fully online Deep Learning (DL) based Intrusion Detection System (IDS) that requires no human intervention or prior off-line learning. The proposed framework analyzes and labels incoming traffic packets based only on the decisions of the IDS itself using an Auto-Associative Deep Random Neural Network, and on an online estimate of its statistically measured trustworthiness. The SSID framework enables IDS to adapt rapidly to time-varying characteristics of the network traffic, and eliminates the need for offline data collection. This approach avoids human errors in data labeling, and human labor and computational costs of model training and data collection. The approach is experimentally evaluated on public datasets and compared with well-known machine learning and deep learning models, showing that this SSID framework is very useful and advantageous as an accurate and online learning DL-based IDS for IoT systems.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/abstract/document/10531267</style></URL><title><style face="normal" font="default" size="100%">Online Self-Supervised Deep Learning for Intrusion Detection Systems</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Godlove Kuaban</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piotr Czekalski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Julius Tangka</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Modelling of the Energy Depletion Process and Battery Depletion Attacks for Battery-Powered Internet of Things (IoT) Devices</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2023</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Sensors</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">23</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">6183</style></ISSUE><DATE><style face="normal" font="default" size="100%">07/2023</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;pre&gt;&#13;
The Internet of Things (IoT) is transforming almost every industry, including agriculture, food processing, health care, oil and gas, environmental protection, transportation and logistics, manufacturing, home automation, and safety. Cost-effective, small-sized batteries are often used to power IoT devices being deployed with limited energy capacity. The limited energy capacity of IoT devices makes them vulnerable to battery depletion attacks designed to exhaust the energy stored in the battery rapidly and eventually shut down the device. In designing and deploying IoT devices, the battery and device specifications should be chosen in such a way as to ensure a long lifetime of the device. This paper proposes diffusion approximation as a mathematical framework for modelling the energy depletion process in IoT batteries. We applied diffusion or Brownian motion processes to model the energy depletion of a battery of an IoT device. We used this model to obtain the probability density function, mean, variance, and probability of the lifetime of an IoT device. Furthermore, we studied the influence of active power consumption, sleep time, and battery capacity on the probability density function, mean, and probability of the lifetime of an IoT device. We modelled ghost energy depletion attacks and their impact on the lifetime of IoT devices. We used numerical examples to study the influence of battery depletion attacks on the distribution of the lifetime of an IoT device. We also introduced an energy threshold after which the device's battery should be replaced in order to ensure that the battery is not completely drained before it is replaced. &lt;/pre&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Modelling of the Energy Depletion Process and Battery Depletion Attacks for Battery-Powered Internet of Things (IoT) Devices</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mert Nakip</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">IoT Network Cybersecurity Assessment with the Associated Random Neural Network</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2023</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE Access</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;This paper proposes a method to assess the security of an n device, or IP address, IoT network by simultaneously identifying all the compromised IoT devices and IP addresses. It uses a specific Random Neural Network (RNN) architecture composed of two mutually interconnected sub-networks that complement each other in a recurrent structure, called the Associated RNN (ARNN). For each of the n devices or IP addresses in the IoT network, two distinct neurons of the ARNN advocate opposite views: compromised or not compromised. The fully interconnected 2 n neuron ARNN structure of paired neurons learns offline from ground truth data. Thus rather than requiring a separate attack detector at each network node, the ARNN offers a single overall attack detector that observes the incoming traffic at each node, learns about the interdependencies between network nodes, and formulates a recommendation for each device or IP address in an IoT network. The ARNN weight initialization and learning algorithm are discussed, and the ARNN performance is evaluated using real attack data, and compared against several learning and testing techniques. Results are obtained both for off-line learning with ground truth data, and for on-line incremental learning using a simplified average metric measured from incoming packet traffic. Comparisons with the best state-of-the-art techniques show that the ARNN significantly outperforms previously known approaches.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">IoT Network Cybersecurity Assessment with the Associated Random Neural Network</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Electricity Consumption by ICT: Facts, trends, and measurements</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2023</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">ACM Ubiquity</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">2023</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">August</style></ISSUE><SECTION><style face="normal" font="default" size="100%">1-15</style></SECTION><DATE><style face="normal" font="default" size="100%">08/2023</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;This paper considers key issues surrounding energy consumption by information and communication technologies (ICT), which has been steadily growing and is now attaining approximately 10% of the worldwide electricity consumption with a significant impact on greenhouse gas emissions. The perimeter of ICT systems is discussed, and the role of the subsystems that compose ICT is considered. Data from recent years is used to understand how each of these sub-systems contribute to ICT's energy consumption. The quantitatively demonstrated positive correlation between the penetration of ICT in the world's different economies and the same economies' contributions to undesirable greenhouse gas emissions is also discussed. The paper also examines how emerging technologies such as 5G, AI, edge computing, and cryptocurrencies are contributing to the worldwide increase in electricity consumption by ICT, despite the increases in ICT efficiency, in terms of energy consumed per bit processed, stored, or transmitted. The measurement of specific ICT systems' electricity consumption is also addressed, and the manner in which this consumption can be minimized in a specific edge computing context is discussed.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Electricity Consumption by ICT: Facts, trends, and measurements</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Godlove Kuaban</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piotr Czekalski</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Modelling the Energy Performance of Off-Grid Sustainable Green Cellular Base Stations</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">2023 31st International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2023)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Stony Brook, NY, USA</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">16/01/2024</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;pre&gt;&#13;
There is a growing awareness of the need to reduce carbon emissions from the operation of mobile networks. The massive deployment of ultra-dense 5G and IoT networks will significantly increase energy demand and put the electricity grid under stress while also driving up operational costs. In this paper, we model the energy performance of an off-grid sustainable green cellular base station site which consists of a solar power system, Battery Energy Storage (BESS) and Hydrogen Energy Storage (HESS) system, and various types of macrocells, microcells, picocells, or femtocells, with broadband optical or microwave transmission systems, and other electrical and electronic systems (air conditioner, power converters, and controllers. We propose diffusion-based models of the charging and discharging processes of the energy storage systems, and obtain the probability of charging them to their full capacities during the day and completely discharging them at the end of each day. We also investigate the impact of design parameters such as the mean charging rate and the mean discharging rate on the probability densities of charging BESS and HESS to their full capacities during the day and of completely discharging them before the end of each night period. &lt;/pre&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Modelling the Energy Performance of Off-Grid Sustainable Green Cellular Base Stations</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>7</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Godlove Kuaban</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Sapana Sharma</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piotr Czekalski</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">A Markov model for a Self-Powered Green IoT Device with State-Dependent Energy Consumption</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Proceeding of the 2023 4th International Conference on Communications, Information, Electronic and Energy Systems (CIEES)</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><ISBN><style face="normal" font="default" size="100%">979-8-3503-3691-7</style></ISBN><ABSTRACT><style face="normal" font="default" size="100%">&lt;pre&gt;&#13;
The growing concerns about the resource sustainability and environmental impact of deploying billions or trillions of IoT devices in the various sectors of society or economy have given rise to an energy research field known as Green-IoT (G-IoT). G-IoT emphasises the need to prioritise energy efficiency and the reduction of pollution in the IoT ecosystem. This paper presents a Markov model for a self-powered Green IoT device with state-dependent energy consumption. The model evaluates the energy performance of a self-powered Green IoT device. We derive the probability distribution of the energy content of the battery, including the probability that the energy stored in the battery is completely depleted and the density of the lifetime of the IoT device. We investigate the influence of some design parameters on the results. Introducing the threshold at which the device is forced to enter an energy-saving regime significantly increases a G-IoT device's lifetime. Apart from deploying energy-efficient mechanisms to increase the lifetime of a G-IoT device, integrating an efficient energy harvesting system into the device will increase its lifetime and reduce the frequency of battery replacement.&lt;/pre&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">A Markov model for a Self-Powered Green IoT Device with State-Dependent Energy Consumption</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Godlove Kuaban</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piotr Pecka</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Valery Nkemeni</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piotr Czekalski</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Energy performance of Internet of Things (IoT) networks for pipeline monitoring</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">2024 International Wireless Communications and Mobile Computing (IWCMC)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Ayia Napa, Cyprus</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">17/07/2024</style></DATE><ISBN><style face="normal" font="default" size="100%">979-8-3503-6126-1</style></ISBN><ABSTRACT><style face="normal" font="default" size="100%">&lt;pre&gt;&#13;
Pipelines are the most convenient ways to transport fluids (e.g., water, oil, and gas). However, leakage of fluids into the environment results in resource wastage (especially water, which is becoming a scarce resource) and environmental pollution (in the case of leakage of toxic fluids like oil and gas). Emerging technologies like the Internet of Things (IoT), Wireless Sensor Networks (WSNs), Artificial Intelligence (AI), distributed computing, and cloud computing enable continuous monitoring of pipelines to detect leakages and corrosion on the pipeline. The main challenge with using battery-powered sensor nodes to monitor pipelines is the energy constraint, necessitating frequent battery replacement. Thus, there is a need to develop energy-saving mechanisms to prolong the lifetime of these sensor nodes. In this paper, we use the diffusion approximation modelling framework in which the data from the experimental testbed are used to model the dynamics of the battery's energy content and to estimate the mean and variance of the device's lifetime. The novelty in the proposed diffusion model of the battery of an IoT node is the introduction of multiple energy thresholds that split the energy state-space of the battery into multiple energy-saving regimes. As the battery discharges, the node gradually transitions into energy-saving regimes by reconfiguring some of its parameters to reduce energy consumption (sometimes at the cost of trading off some performance metrics). We investigate the impact of energy-saving regimes or the number of thresholds on the node's lifetime.&lt;/pre&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Energy performance of Internet of Things (IoT) networks for pipeline monitoring</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Godlove Kuaban</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piotr Czekalski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Valery Nkemeni</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Energy Performance of Off-Grid Green Cellular Base Stations</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Performance Evaluation</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">165</style></VOLUME><DATE><style face="normal" font="default" size="100%">10/07/2024</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;pre&gt;&#13;
The most energy-hungry parts of mobile networks are the base station sites, which consume around $60-80\%$ of their total energy. One of the approaches for relieving this energy pressure on the electricity grid infrastructure and reducing the Operational Expenditures (OPEX) is to power base stations with renewable energy. However, the design of a green mobile network requires the dimensioning of the energy harvesting and storage systems through the estimation of the network's energy demand. Therefore, this paper develops a diffusion-based modelling framework for solar-powered green off-grid base station sites. We apply this framework to evaluate the energy performance of homogeneous and hybrid energy storage systems supplied by harvested solar energy. We present the complete analysis, with numerical examples, to study the relationship between the design parameters and the energy performance metrics. The numerical computations demonstrate how the proposed framework can be applied to evaluate homogeneous and unconventional hybrid energy storage systems.&lt;/pre&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Energy Performance of Off-Grid Green Cellular Base Stations</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Miltiadis Siavvas</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dimitrios Tsoukalas</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Charalambos Marantos</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Lazaros Papadopoulos</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Christos Lamprakos</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Oliviu Matei</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Christos Strydis</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Muhammad Siddiqi</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Philippe Chrobocinski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Katarzyna Filus</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Joanna Domańska</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Paris Avgeriou</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Apostolos Ampatzoglou</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dimitrios Soudris</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Alexander Chatzigeorgiou</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dionysios Kehagias</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dimitrios Tzovaras</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">SDK4ED: a platform for building energy efficient, dependable, and maintainable embedded software</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Automated Software Engineering</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><title><style face="normal" font="default" size="100%">SDK4ED: a platform for building energy efficient, dependable, and maintainable embedded software</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mert Nakip</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Miltiadis Siavvas</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">System-wide vulnerability of multi-component software</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%"/></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">196</style></VOLUME><SECTION><style face="normal" font="default" size="100%">110453</style></SECTION><DATE><style face="normal" font="default" size="100%">10/2024</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;In software systems comprised of many interconnected components, the vulnerability of each component will affect the vulnerability of other components and of the system as a whole. Existing techniques allow the quantification of the vulnerability of individual components taken singly, but the assessment of their vulnerability when they are interconnected or interdependent remains a challenge. The present work addresses this problem with a novel System-Wide Vulnerability Assessment (SWVA) framework for interconnected software components, based on an Associated Random Neural Network (ARNN) that estimates the system-wide vulnerability of all software components from known local vulnerabilities of individual components, and from their interconnections. The ARNN uses a problem-specific weight initialization, and learns from existing software system examples with a gradient-based deep learning algorithm. The ARNN is then used to assess the vulnerability of hitherto unseen software systems. The performance of the proposed ARNN-based SWVA framework is evaluated and compared against several well-known machine learning techniques on 13 different versions of a real-world software system with up to 11 components. The experimental results show the superior performance of the ARNN achieving above 85% median accuracy and good high scalability with respect to the number of connected software components.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">System-wide vulnerability of multi-component software</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Baran Gül</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mert Nakip</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">DISFIDA: Distributed Self-Supervised Federated Intrusion Detection Algorithm with Online Learning for Health Internet of Things and Internet of Vehicles  </style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Internet of Things</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">28</style></VOLUME><SECTION><style face="normal" font="default" size="100%">101340</style></SECTION><DATE><style face="normal" font="default" size="100%">12/2024</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Networked health systems are often the victims of cyberattacks with serious consequences for patients and healthcare costs, with the Internet of Things (IoT) being an additional prime target. In future systems we can imagine that the Internet of Vehicles (IoV) will also be used for conveying patients for diagnosis and treatment in an integrated manner. Thus the medical field poses very significant and specific challenges since even for a single patient, several providers may carry out tests or offer healthcare services, and may have distinct interconnected sub-contractors for services such as ambulances and connected cars, connected devices or temporary staff providers, that have distinct confidentiality requirements on top of possible commercial competition. On the other hand, these distinct entities can be subject to similar or coordinated attacks, and could benefit from each others’ cybersecurity experience to better detect and mitigate cyberattacks. Thus the present work proposes a novel Distributed Self-Supervised Federated Intrusion Detection Algorithm (DISFIDA), with Online Self-Supervised Federated Learning, that uses Dense Random Neural Networks (DRNN). In DISFIDA learning data is private, and neuronal weights are shared among Federated partners. Each partner in DISFIDA combines its synaptic weights with those it receives other partners, with a preference for those weights that have closer numerical values to its own weights which it has learned on its own. DISFIDA is tested with three open-access datasets against five benchmark methods, for two relevant IoT healthcare applications: networks of devices (e.g., body sensors), and Connected Smart Vehicles (e.g., ambulances that transport patients). These tests show that the DISFIDA approach offers 100% True Positive Rate for attacks (one percentage point better than comparable state of the art methods which attain 99%) so that it does better at detecting attacks, with 99% True Negative Rate similar to state-of-the-art Federated Learning, for Distributed Denial of Service (DDoS) attacks.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">DISFIDA: Distributed Self-Supervised Federated Intrusion Detection Algorithm with Online Learning for Health Internet of Things and Internet of Vehicles  </style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Mohammed Nasereddin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mert Nakip</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Deep Learning Intrusion Detection and Mitigation of DoS Attacks</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">32nd International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS24)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Krakow, Poland</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Internet of Things (IoT) networks are highly vulnerable to common network DoS and DDoS attacks, which flood limited system resources or IoT devices, overwhelming them with large numbers of attack packets. In order to mitigate such attacks, this paper develops a lightweight yet effective Intrusion Detection and Prevention System (IDPS), that sequentially detects and mitigates the attack via a Deep Random Neural Network (DRNN) and a Drop-Idle-Repeat process. The IDPS is evaluated for UDP Floods, attacks on an experimental test-bed. The results show that UDP Flood attacks can be mitigated with the proposed IDPS, allowing the system to continue routine operations, and resume communications when the attack ends.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/abstract/document/10786560</style></URL><title><style face="normal" font="default" size="100%">Deep Learning Intrusion Detection and Mitigation of DoS Attacks</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Godlove Kuaban</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piotr Pecka</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Sapana Sharma</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Pradeep Singh</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Valery Nkemeni</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piotr Czekalski</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Energy performance of self-powered Green IoT nodes</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Frontiers in Energy Research</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">12</style></VOLUME><DATE><style face="normal" font="default" size="100%">07/11/2024</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;The widespread adoption of the Internet of Things (IoT) partly depends on the successful design and deployment of IoT nodes that can operate for several years without any service outage and the need to replace their energy storage systems (e.g., battery, capacitor, or supercapacitor) when all the stored energy is depleted or when the cycle life of the Energy Storage Systems (ESS) is reached. Replacing batteries in the case of large-scale IoT networks and nodes located in places that are hard to reach is very challenging and costly, requiring the design of IoT nodes that can operate for several years without the need for human intervention. One such example is the deployment of IoT nodes in large agricultural fields (for soil or crop monitoring) or a long-distance pipeline (for pipeline monitoring). This paper investigates the practical implications of imposing energy-saving thresholds on the energy performance metrics of green IoT nodes. We propose an energy packet-based model for the evaluation of the energy performance of a green IoT node with the possibility of switching the node to energy-saving regimes on the fly when the energy content of the ESS reaches defined thresholds. Configuring single or multiple thresholds improves the energy performance of the node significantly (e.g., increases the lifetime of the node and reduces the probability of service outage and energy wastage), and the value of the threshold(s) should be carefully chosen. The energy performance of the IoT node can also be improved by dimensioning the energy harvesting system to ensure that the node operates for several years without running out of energy (e.g., maximizing the lifetime of the nodes and minimising the probability of service outage and energy wastage).&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Energy performance of self-powered Green IoT nodes</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Godlove Kuaban</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piotr Pecka</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Valery Nkemeni</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piotr Czekalski</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Impact of energy leakage on the energy performance of green IoT nodes</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">32nd International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2024)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Krakow, Poland</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;In the present paper, we investigate the impact of imperfections (non-idealities) of the energy storage system (e.g., batteries, capacitors, or supercapacitors) on the energy performance of green IoT nodes. In particular, we investigate the impact of energy leakage from the Energy Storage System (ESS) on important energy performance metrics, such as service outage probability, the density of the lifetime of the node, and the time-dependent mean number of energy packets (EPs) in the ESS. Also, we explore various strategies that can be employed to compensate for the impact of energy losses due to energy leakage on the energy performance metrics. Specifically, we examine two potential strategies for improving the energy performance of the IoT nodes: (i) through increasing the energy generation rate of the energy harvesters (e.g., by adding additional solar panels or replacing the existing solar panels with more efficient ones that can produce more energy), and (ii) through reducing the energy consumption rate of the IoT node (e.g., by configuring ESS energy thresholds below which the nodes are forced to operate in low energy consumption states).&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/10786519 </style></URL><title><style face="normal" font="default" size="100%">Impact of energy leakage on the energy performance of green IoT nodes</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Guoliang Zhou</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Yijia Liu</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Zheng Yan</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Is ChatGPT Trustworthy Enough? A Review</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE Consumer Electronics Magazine </style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;ChatGPT, as an advanced model that seamlessly integrates into diverse digital interactions,&lt;br /&gt;&#13;
shows great potential to enhance the performance of consumer technology and reshape its&lt;br /&gt;&#13;
landscape. The critical question of its trustworthiness and associated challenges becomes&lt;br /&gt;&#13;
increasingly prominent. However, the literature still lacks a thorough review to study its trust. This&lt;br /&gt;&#13;
comprehensive review explores whether ChatGPT is trustworthy enough by navigating the&lt;br /&gt;&#13;
multifaceted realm of large language models, placing a significant focus on its exemplar model,&lt;br /&gt;&#13;
ChatGPT. Our exploration traverses the complex interplay of factors impacting user trust, including&lt;br /&gt;&#13;
trust in ChatGPT itself and trust in its utilization and dissemination. By delving into insightful&lt;br /&gt;&#13;
perspectives on the ChatGPT trustworthiness regarding a set of evaluation criteria including&lt;br /&gt;&#13;
fundamental properties, subjective properties, security and privacy, we find that ChatGPT is far from&lt;br /&gt;&#13;
trustworthy based on related literature review. This paper sheds light on the weakness of ChatGPT&lt;br /&gt;&#13;
trust and provides the trajectory of future development in the realm of large language models&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://www.researchgate.net/publication/385065063_Is_ChatGPT_Trustworthy_Enough_A_Review#fullTextFileContent</style></URL><title><style face="normal" font="default" size="100%">Is ChatGPT Trustworthy Enough? A Review</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Jacob Bergquist</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Karl Sigman</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">On an Adaptive-Quasi-Deterministic Transmission Policy Queueing Model</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">32nd IEEE MASCOTS'24 Conference on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, October 21-23, 2024, Krakow Poland</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEEXplore</style></PUBLISHER><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;We analyze, further and deeper, a recently proposed&lt;br /&gt;&#13;
technique for addressing the Massive Access Problem (MAP), an&lt;br /&gt;&#13;
issue in telecommunications which arises when too many devices&lt;br /&gt;&#13;
transmit packets to a gateway in quick succession. This technique,&lt;br /&gt;&#13;
the Adaptive-Quasi-Deterministic Transmission Policy (AQDTP)&lt;br /&gt;&#13;
is a special case of “traffic shaping” which involves delaying&lt;br /&gt;&#13;
some packets at the points of origin to alleviate congestion at&lt;br /&gt;&#13;
the routers. One nice feature of AQDTP is that it loses no&lt;br /&gt;&#13;
packets and allows an infinite buffer. In this work, to clarify&lt;br /&gt;&#13;
the approach in a general queueing theory framework, and to&lt;br /&gt;&#13;
move beyond the original telecommunications application, we&lt;br /&gt;&#13;
frame these potential delays as time spent at a caf´e by customers&lt;br /&gt;&#13;
before proceeding to a service facility. We first present some&lt;br /&gt;&#13;
sample-path results that significantly refine and expand upon&lt;br /&gt;&#13;
what was shown in previous work, and then present further&lt;br /&gt;&#13;
results under a general stationary ergodic stochastic framework.&lt;br /&gt;&#13;
In the sample-path realm, we give conditions that ensure AQDTP&lt;br /&gt;&#13;
will not change the total delay and sojourn time of any customer&lt;br /&gt;&#13;
as compared to what that customer would have experienced if&lt;br /&gt;&#13;
there was no caf´e; but we also prove that AQDTP can never&lt;br /&gt;&#13;
reduce the total delay. The difference is that, under AQDTP, some&lt;br /&gt;&#13;
of that delay is spent at the caf´e instead of in the queue/line at the&lt;br /&gt;&#13;
service facility. In a stochastic framework, our focus is on stability&lt;br /&gt;&#13;
and constructing proper stationary versions of the model. Under&lt;br /&gt;&#13;
i.i.d. assumptions we dig deeper by proving Harris recurrence&lt;br /&gt;&#13;
of an underlying two-dimensional Markov process, and explicitly&lt;br /&gt;&#13;
find positive recurrent regeneration points.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://www.researchgate.net/publication/384288968_On_an_Adaptive-Quasi-Deterministic_Transmission_Policy_Queueing_Model#fullTextFileContent</style></URL><title><style face="normal" font="default" size="100%">On an Adaptive-Quasi-Deterministic Transmission Policy Queueing Model</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Yuting Ma</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">K Liu</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Impact of IoT system imperfections and passenger errors on cruise ship evacuation delay</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">sensors</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">24</style></VOLUME><DATE><style face="normal" font="default" size="100%">03/2024</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Cruise ships and other naval vessels include automated Internet of Things (IoT)-based evacuation systems for the passengers and crew to assist them in case of emergencies and accidents. The technical challenges of assisting passengers and crew to safety during emergencies include various aspects such as sensor failures, imperfections in the sound or display systems that are used to direct evacuees, the timely selection of optimum evacuation routes for the evacuees, as well as computation and communication delays that may occur in the IoT infrastructure due to intense activities during an emergency. In addition, during an emergency, the evacuees may be confused or in a panic, and may make mistakes in following the directions offered by the evacuation system. Therefore, the purpose of this work is to analyze the effect of two important aspects that can have an adverse effect on the passengers’ evacuation time, namely (a) the computer processing and communication delays, and (b) the errors that may be made by the evacuees in following instructions. The approach we take uses simulation with a representative existing cruise ship model, which dynamically computes the best exit paths for each passenger, with a deadline-driven Adaptive Navigation Strategy (ANS). Our simulation results reveal that delays in the evacuees’ reception of instructions can significantly increase the total time needed for passenger evacuation. In contrast, we observe that passenger behavior errors also affect the evacuation duration, but with less effect on the total time needed to evacuate passengers. These findings demonstrate the importance of the design of passenger evacuation systems in a way that takes into account all realistic features of the ship’s indoor evacuation environment, including the importance of having high-performance data processing and communication systems that will not result in congestion and communication delays.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%"> https://doi.org/10.3390/s24061850</style></URL><title><style face="normal" font="default" size="100%">Impact of IoT system imperfections and passenger errors on cruise ship evacuation delay</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Yuting Ma</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Kezhong Liu</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">IoT Performance for Maritime Passenger Evacuation</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">2024 IEEE 10th World Forum on Internet of Things (WF-IoT)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Ottawa, ON, Canada</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">11/2024</style></DATE><title><style face="normal" font="default" size="100%">IoT Performance for Maritime Passenger Evacuation</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Minqiang Yang</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Edith Ngai</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Xiping Hu</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Bin Hu</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jiangchuan Liu</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Victor Leung</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Digital Phenotyping and Feature Extraction on Smartphone Data for Depression Detection</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Proceedings of the IEEE</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">112</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">12</style></ISSUE><SECTION><style face="normal" font="default" size="100%">1773-1798</style></SECTION><DATE><style face="normal" font="default" size="100%">12/2024</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Smartphones are widely used as portable data collectors for wearable and healthcare sensors that can passively collect data streams related to the environment, health status, and behaviors. Recent research shows that the collected data can be used to monitor not only the physical states but also the mental health of individuals. However, extracting the features of digital phenotypes that characterize major depressive disorder (MDD) is technically challenging and may raise significant privacy concerns. Addressing such challenges has become the focus of many researchers. This article provides a comprehensive analysis of several key issues related to ubiquitous sensing to aid in detecting MDD. Specifically, this article analyzes existing methodologies and feature extraction algorithms used to detect possible MDD through digital phenotyping from smartphone data. In particular, five types of features are summarized and explained, namely, location, movement, rhythm, sleep, and social and device usage. Finally, related limitations and challenges are discussed to provide paths for further research and engineering.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Digital Phenotyping and Feature Extraction on Smartphone Data for Depression Detection</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Minimizing delay and power consumption at the edge</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2025</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Sensors</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">25</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">2</style></ISSUE><SECTION><style face="normal" font="default" size="100%">502</style></SECTION><DATE><style face="normal" font="default" size="100%">02/2025</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Edge computing systems must offer low latency at low cost and low power consumption for sensors and other applications, including the IoT, smart vehicles, smart homes, and 6G. Thus, substantial research has been conducted to identify optimum task allocation schemes in this context using non-linear optimization, machine learning, and market-based algorithms. Prior work has mainly focused on two methodologies: (i) formulating non-linear optimizations that lead to NP-hard problems, which are processed via heuristics, and (ii) using AI-based formulations, such as reinforcement learning, that are then tested with simulations. These prior approaches have two shortcomings: (a) there is no guarantee that optimum solutions are achieved, and (b) they do not provide an explicit formula for the fraction of tasks that are allocated to the different servers to achieve a specified optimum. This paper offers a radically different and mathematically based principled method that explicitly computes the optimum fraction of jobs that should be allocated to the different servers to (1) minimize the average latency (delay) of the jobs that are allocated to the edge servers and (2) minimize the average energy consumption of these jobs at the set of edge servers. These results are obtained with a mathematical model of a multiple-server edge system that is managed by a task distribution platform, whose equations are derived and solved using methods from stochastic processes. This approach has low computational cost and provides simple linear complexity formulas to compute the fraction of tasks that should be assigned to the different servers to achieve minimum latency and minimum energy consumption.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://www.mdpi.com/1424-8220/25/2/502</style></URL><title><style face="normal" font="default" size="100%">Minimizing delay and power consumption at the edge</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mert Nakip</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Miltiadis Siavvas</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">System Wide Vulnerability and Trust in Multi-Component Communication System Software</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE Network </style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">39</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">2</style></ISSUE><SECTION><style face="normal" font="default" size="100%">108-114</style></SECTION><DATE><style face="normal" font="default" size="100%">09/2024</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;In the software rich environment of 6G,&#160;systems will be surrounded by edge devices that support distributed software&#160;systems which are critical to&#160;operations. Such systems may also be subject to frequent&#160;updates or uploads of individual software components.&#160;Trust in such systems will therefore depend on our ability&#160;to rapidly ensure that such software is not vulnerable to&#160;cyberattacks or malicious compromises. Thus this paper&#160;presents a novel System-Wide Vulnerability Assessment&#160;(SWVA) framework based on Machine Learning, that&#160;can be frequently activated to assess the vulnerability of&#160;interconnected software components over edge systems.&#160;The performance of the SWVA framework is illustrated&#160;by assessing the vulnerability of 13 versions of a real world 11 component software system, and comparingthe ARNN results against the well-known ML models&#160;MLP, KNN, and Lasso. The results show the superior&#160;performance of SWVA, offering over 85% median accuracy&#160;and good scalability as the number of connected software&#160;components increases.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://drive.google.com/file/d/1123-TES0XmyG8_l1XlmAfdWV5Jf6QPeo/view </style></URL><title><style face="normal" font="default" size="100%">System Wide Vulnerability and Trust in Multi-Component Communication System Software</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mohammed Nasereddin</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Adaptive attack mitigation for IoV Flood Attacks</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2025</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE  Internet of Things Journal</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">12</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">5</style></ISSUE><SECTION><style face="normal" font="default" size="100%">4701-4714</style></SECTION><DATE><style face="normal" font="default" size="100%">03/2025</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Gateway Servers for the Internet of Vehicles (IoV)&#160;must meet stringent Security and Quality of Service (QoS)&#160;requirements, including cyberattack protection, low delays and&#160;minimal packet loss, to offer secure real-time data exchange for&#160;human and vehicle safety and efficient road traffic management.&#160;Therefore, it is vital to protect these systems from cyberattacks&#160;with adequate Attack Detection (AD) and Mitigation mechanisms. Such attacks often include packet Floods that impair&#160;the QoS of the networks and Gateways and even impede the&#160;Gateways’ capability to carry out AD. Thus, this paper first&#160;evaluates these effects using system measurements during Flood&#160;attacks. It then demonstrates how a Smart Quasi-Deterministic&#160;Policy Forwarder (SQF) at the entrance of the Gateway can&#160;regulate the incoming traffic to ensure that the Gateway supports&lt;br /&gt;&#13;
the AD to operate promptly during an attack. Since Flood attacks&#160;create substantial packet backlogs, we propose a novel Adaptive&#160;Attack Mitigation (AAM) system that is activated after an attack&#160;is detected to dynamically sample the incoming packet stream,&#160;determine whether the attack is continuing, and also drop batches&#160;of packets at the input to reduce the effects of the attack. The&#160;AAM is designed to minimize a cost function that includes the&#160;sampling overhead and the cost of lost benign packets. We show&#160;experimentally that the Optimum AAM approach is effective&#160;in mitigating attacks and present theoretical and experimental&#160;results that validate the proposed approach.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%"> https://drive.google.com/file/d/1UPsgRyMDKUv4sT6oiOQQXxDgQ8bFTXQM/view</style></URL><title><style face="normal" font="default" size="100%">Adaptive attack mitigation for IoV Flood Attacks</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Zhaozheng Li</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jiahe Lan</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Zheng Yan</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Backdoor attacks and defense mechanisms in federated learning: A survey</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2025</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Information Fusion </style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">123</style></VOLUME><ISSUE><style face="normal" font="default" size="100%"> 103248 </style></ISSUE><DATE><style face="normal" font="default" size="100%">05/2025</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Federated Learning (FL) is a distributed machine learning framework that enables the collaborative training of machine learning models by multiple entities. However, FL is vulnerable to various potential risks, especially backdoor attacks. A backdoor attack aims to implant hidden backdoors into a global model by compromising one or more clients and making them provide poisoned model updates. Consequently, the global model misclassifies inputs with triggers as adversary-desired classes/labels while performing well on benign inputs. Despite its severity, existing literature lacks a comprehensive review on backdoor attacks and their defense mechanisms of FL, especially for vertical FL. This paper comprehensively reviews and evaluates recent advances in backdoor attacks and defense mechanisms on FL. We first introduce foundational concepts about FL, backdoor attacks, and defense mechanisms, along with their respective security models. Then, we propose two sets of evaluation criteria that a sound backdoor attack and a defense mechanism should meet, respectively. After that, we provide taxonomies of existing backdoor attacks and defense mechanisms of FL and review them by employing the proposed criteria to evaluate their pros and cons. We also explore a positive application of backdoors in FL, i.e., backdoor-based watermarking. Finally, we discuss a number of open issues and suggest promising future research directions.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://www.sciencedirect.com/science/article/pii/S1566253525003215</style></URL><title><style face="normal" font="default" size="100%">Backdoor attacks and defense mechanisms in federated learning: A survey</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Tingxi Luo</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jie Wang</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Zheng Yan</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Graph Neural Networks for Trust Evaluation: Criteria, State-of-the-Art, and Future Directions</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2025</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE Network (Early Access), IEEEXplore</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><DATE><style face="normal" font="default" size="100%">03/2025</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;The process of quantifying trust considers the factors that affect it, which can be applied to identify malicious behavior, reduce uncertainty, and facilitate decision-making. Traditional trust evaluation methods based on statistics and reasoning, rely heavily on domain knowledge, which limits their practical applications. Graph Neural Networks (GNNs) are a new Machine Learning (ML) paradigm that can revolutionize the evaluation of trust, by modeling relationships as graphs to simplify relevant data and automating end-to-end evaluation. Thus, a variety of GNN-based trust evaluation models have been developed for different applications. However, there is still a gap in the literature regarding a review on these advances with discussion about remaining challenges. To bridge this gap, we conduct the first review on GNN-based trust evaluation. We first propose a set of criteria in terms of trust-related attributes, correctness, functionality, and overhead. Then, we propose a taxonomy of existing GNN-based trust evaluation models, followed by a review using the proposed criteria to analyze their pros and cons. A quantitative analysis of the recent cutting-edge models is also provided. Based on the review and experimental results, we identify key challenges and suggest future research directions.&#160;&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/10925363 </style></URL><title><style face="normal" font="default" size="100%">Graph Neural Networks for Trust Evaluation: Criteria, State-of-the-Art, and Future Directions</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Zitong Zhang</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Haoxiang Han</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Zheng Yan</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">A Survey on Cross-Shard Transaction Allocation Optimization and Processing Mechanisms</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2025</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE Communications Surveys and Tutorials</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Sharding is one of the most effective technologies for improving blockchain scalability by dividing a blockchain network into multiple shards that process transactions in parallel. However, many existing sharded blockchain systems can include a large number of cross-shard transactions, resulting in high communication overhead and processing latency. Two solutions have emerged to address these issues. One of them focuses on optimizing the allocation of transactions to reduce the cross-shard communication while ensuring workload balance. The other emphasizes the design of an efficient cross-shard transaction processing mechanism to reduce the latency caused by confirming cross-shard transactions. However, the literature still lacks a comprehensive review on the advances of Cross-shard Transaction Allocation Optimization and Processing (CTAOP) mechanisms, which would be a valuable addition to the literature. This paper is a comprehensive survey of existing CTAOP mechanisms, and first introduces cross-shard transactions and related concepts. Then, we propose two sets of criteria for evaluating the two types of CTAOP mechanisms, and provide a taxonomy of CTAOP mechanisms, followed by a thorough review based on our proposed criteria. A list of open issues is also highlighted, and corresponding future research directions are suggested to advance our understanding of CTAOP mechanisms.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">A Survey on Cross-Shard Transaction Allocation Optimization and Processing Mechanisms</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Godlove Kuaban</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piotr Czekalski</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Time-Dependent Analysis of the Interaction Between Energy Harvesting, Leakage, and Consumption Processes in a Green IoT Device</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2025</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%"> IEEE Access </style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;The Internet of Things (IoT) is transforming industries by enhancing productivity and efficiency; however, energy availability remains a significant challenge due to the limited capacity of batteries and supercapacitors powering IoT devices. The emergence of Green IoT (G-IoT) frameworks, which prioritize energy efficiency and renewable energy integration, offers a promising solution to address this challenge. Despite these advancements, energy storage systems (ESSs) face issues such as capacity degradation, leakage, and charge redistribution, which can lead to energy depletion and service disruptions. Traditional models often assume constant energy harvesting rates, overlooking the time-varying nature of environmental conditions that influence energy availability. In this paper, we propose a novel mathematical framework that incorporates time-dependent fluctuations in the energy harvesting rate to analyse the dynamic interactions between energy harvesting, leakage, and consumption in green IoT systems. Our approach includes an Energy Packet Network (EPN) model to represent transient energy dynamics and a Markov model to capture fluctuations in the energy harvesting rate. Through numerical simulations, we evaluate the impact of key design parameters, such as ESS capacity, mean energy consumption rate, energy leakage, and energy harvesting rate variations, on critical performance metrics including the probability of energy depletion and the transient mean number of stored energy packets. The results highlight the importance of considering time-varying energy harvesting in the design and optimization of IoT systems for long-term operation and sustainability.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Time-Dependent Analysis of the Interaction Between Energy Harvesting, Leakage, and Consumption Processes in a Green IoT Device</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Godlove Kuaban</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piotr Pecka</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piotr Czekalski</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Energy Dynamics of Green IoT Nodes with Time-Varying Energy Harvesting, Leakage, and Consumption Patterns</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2025</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">33rd International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication System</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Paris, France</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">12/2025</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;The growing proliferation of Internet of Things (IoT) devices has intensified the need for sustainable energy solutions, particularly in resource-constrained deployments where non-rechargeable batteries and supercapacitors are the primary energy sources. Green IoT (G-IoT) frameworks address this challenge by combining energy-saving techniques with energy harvesting from ambient sources such as solar power. However, the intermittent nature of renewable energy and the non-ideal behavior of energy storage systems—such as energy leakage and capacity degradation—complicate reliable energy provisioning. This paper presents a novel Markovian framework for modelling the coupled dynamics of time-varying solar energy harvesting, time-dependent energy consumption, and state-dependent energy leakage in G-IoT systems. Unlike traditional steady-state models, our approach uses Discrete-Time Markov Chains (DTMCs) to capture the stochastic variability in both energy harvesting and consumption processes. We also introduce a refined leakage model in which the leakage rate is dynamically dependent on the stored energy level, enabling a more realistic characterization of energy losses due to energy leakage. Through extensive analytical evaluation, we examine how key parameters—such as storage capacity, leakage rate coefficient, and energy harvesting and consumption patterns—affect critical performance metrics, including the mean stored energy and energy-related service outage probability. Furthermore, we propose a parameter tuning strategy to optimize energy reliability and storage efficiency. The proposed model provides valuable insights for the design and optimization of robust, energy-aware IoT systems powered by renewable energy sources.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Energy Dynamics of Green IoT Nodes with Time-Varying Energy Harvesting, Leakage, and Consumption Patterns</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Mohammed Nasereddin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Parameter Sensitivity of AADRNN-Based Attack Detection</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><SECONDARY_TITLE><style face="normal" font="default" size="100%">31st Symposium on Computers and Communications (IEEE ISCC 2026)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Vilamoura, Algarve, Portugal</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;The growing prevalence of cyberattacks against network-connected systems increases the need for accurate intrusion and attack detection (AD) systems that can react rapidly at the network access ports. Machine learning has often been used for AD because of the high accuracy it can offer, and its fast testing speed for real-time attack detection. On the other hand, the data sampling methods used to transform incoming traffic streams into data for real-time testing by AD systems can have an important effect on the detection accuracy, and are seldom discussed in the literature. Here, we address this often neglected aspect, with the Auto-Associative Dense Random Neural Network (AADRNN), a state-of-the-art AD system. After briefly reviewing the literature and comparing the AADRNN attack detector with other state-of-the-art approaches, this paper focuses on choosing the values of the data sampling parameters and the decision threshold to obtain the best accuracy from this AD method. Experiments regarding Botnet attack detection using the Mirai dataset, as well as synthetic flood attacks conducted on an experimental IoT test-bed are used to illustrate the results, and to guide the parameter choices that maximize the AADRNN’s True Positive Rate and minimize its False Positive Rate.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://ieee-iscc.computer.org/2026/</style></URL><title><style face="normal" font="default" size="100%">Parameter Sensitivity of AADRNN-Based Attack Detection</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Q. Ren</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Z. Yan</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">A Probabilistic Dynamic Network Trust Model for IoT Systems with Lost Messages and Cyberattacks</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><SECONDARY_TITLE><style face="normal" font="default" size="100%">33rd International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, Paris, France</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Paris</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">2025</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;trust model, the Random Neural Network Trust Model (RNNTM),&#160;which incorporates the dynamics of trust formation in&#160;a network through a sequence of “votes” from each entity&#160;regarding all other entities. The model assures fairness among&#160;entities through a fixed replenishment rate of “voting rights”&#160;for each entity, whose voting rights are reduced each time the&#160;entity votes. A positive vote received by an entity increases&lt;br /&gt;&#13;
its voting rights and its trustworthiness, while a negative vote&#160;reduces its voting rights and also its trustworthiness, and a&#160;non-negative integer represents each entity’s instantaneous “trust&#160;value”. An important property of the RNNTM is that an entity&lt;br /&gt;&#13;
cannot express its trust or distrust of the other entities, and&#160;hence affect their trust values, when its trust level is down to&#160;zero, so that untrustworthy entities are not allowed to express&#160;trust or distrust. After developing the theoretical characteristics&lt;br /&gt;&#13;
for the RNNTM model, this paper details its use to evaluate&#160;the trust value of multiple entities in a network of Internet of&#160;Things (IoT) devices and gateways, where&#160;cyberattacks against&#160;the gateways, and messages that should be received from IoT&#160;devices at regular intervals, modify the parameters that express&#160;the trust or distrust between entities. To illustrate its use for&#160;a network of interaction gateways, servers and user equipment,&#160;several detailed time-dependent simulations of the RNNTM are&#160;conducted in the presence of cyberattacks.&#160;&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">A Probabilistic Dynamic Network Trust Model for IoT Systems with Lost Messages and Cyberattacks</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Omer Abdelrahman</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Signalling Storms and Taylor’s Law</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><SECONDARY_TITLE><style face="normal" font="default" size="100%">33rd International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, Paris, France</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Paris</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">2025</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Signalling storms have occurred in several generations&lt;br /&gt;&#13;
of mobile networks due to malevolent end users, as well as&lt;br /&gt;&#13;
a consequence of service disruptions and system failures. Due to&lt;br /&gt;&#13;
the massive increase in the volume of traffic and the diversity of&lt;br /&gt;&#13;
end users and applications in 6G, including the massive access of&lt;br /&gt;&#13;
the Internet of Things, signalling storms and other cyberattacks&lt;br /&gt;&#13;
will require attention with research on adequate detection and&lt;br /&gt;&#13;
mitigation schemes, as we move towards the next generation of&lt;br /&gt;&#13;
mobile networks. Thus, in the present paper, we conduct discrete&lt;br /&gt;&#13;
event simulations of a signalling storm, measure its effects in a&lt;br /&gt;&#13;
simulated environment, and observe that it results in deviations&lt;br /&gt;&#13;
from the well-known and naturally observed statistical property&lt;br /&gt;&#13;
known as “Taylor’s Law”. We then discuss a mathematical model&lt;br /&gt;&#13;
of storms, linking the fraction of malevolent or malfunctioning&lt;br /&gt;&#13;
users to the rate of call establishment requests, and show that&lt;br /&gt;&#13;
the mathematical model also exhibits a violation of Taylor’s&lt;br /&gt;&#13;
Law. Since the detection of cyberattacks from network behaviour&lt;br /&gt;&#13;
parameters is a challenging problem, we suggest that the violation&lt;br /&gt;&#13;
of Taylor’s Law may be a useful tool for designing detectors for&lt;br /&gt;&#13;
signalling attacks, or other network attacks or malfunctions.&lt;br /&gt;&#13;
Index Terms—Attack Detection and Mitigation, 6G, Signalling&lt;br /&gt;&#13;
Attacks, Taylor’s Law&lt;br /&gt;&#13;
&#160;&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Signalling Storms and Taylor’s Law</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mohammed Nasereddin</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Data Driven Optimum Cyberattack Mitigation</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2025</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE DSAA'25: Data Science and Advanced Analytics Conference, Birmingham, UK</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Gateways to the Internet of Things (IoT) are typically servers that communicate with IoT devices, providing them with low-latency services, and connecting them to the internet and other backbone networks. Since IoT devices are often simple and have limited storage and computational capabilities, gateways can be equipped with Attack Detection (AD) software to analyze incoming traffic, detect potential cyberattacks, and protect both the gateway and connected devices from threats that could overwhelm the system as a whole. This paper presents an enhanced gateway system that combines a traffic shaping technique with an attack detection module and an optimum attack mitigation scheme aimed at protecting the gateway and the overall system from cyberattacks. The optimum mitigation approach selects a sampling interval for the AD, that minimizes the total overhead of AD and mitigation. The proposed approach is implemented in a practical test-bed, so that the performance of the mitigation scheme may be evaluated in the presence of flood attacks. The experiments show its practical value and illustrate the agreement obtained between the analysis and the measurements obtained from several experiments.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Data Driven Optimum Cyberattack Mitigation</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Jacob Bergquist</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mohammed Nasereddin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Karl Sigman</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Mitigating massive access with Quasi-Deterministic Transmission: Experiments and stationary analysis</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2025</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Performance Evaluation </style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">170</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">102512</style></ISSUE><SECTION><style face="normal" font="default" size="100%">1 - 12</style></SECTION><DATE><style face="normal" font="default" size="100%">11/2025</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;The Massive Access Problem arises due to devices that forward packets simultaneously to servers in rapid succession, or by malevolent software in devices that flood network nodes with high-intensity traffic. To protect servers from such events, attack detection (AD) software is installed on servers, and the Quasi-Deterministic Transmission Policy (QDTP) has been proposed to “shape traffic” and protect servers, allowing attack detection to proceed in a timely fashion by delaying some of the incoming packets individually based on their arrival times. QDTP does not cause packet loss, and can be designed so that it does not increase end-to-end packet delay. Starting with measurements taken on an experimental test-bed where the QDPT algorithm is installed on a dedicated processor, which precedes the server itself, we show that QDPT protects the server from attacks by accumulating arriving packets at the input of the QDTP processor, then forwarding them at regular intervals to the server. We compare the behaviour of the server, with and without the use of QDTP, showing the improvement it achieves, provided that its “delay” parameter is correctly selected. We analyze the sample paths associated with QDTP and prove that when its delay parameter is chosen in a specific manner, the end-to-end delay of each packet remains unchanged as compared to an ordinary First-In-First-Out system. An approach based on stationary ergodic processes is developed for the stability conditions. Assuming mutually independent and identically distributed inter-arrival times, service times and QDTP delays, we exhibit the positive recurrent structure of a two-dimensional Markov process and its regeneration points.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Mitigating massive access with Quasi-Deterministic Transmission: Experiments and stationary analysis</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Mert Nakip</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">An Associated Random Neural Network Detects Intrusions and Estimates Attack Graphs</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">32nd International Conference on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Krakow, Poland</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">12/2024</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Cyberattacks, especially Botnet Distributed Denial of Service (DDoS), increasingly target networked systems, compromise interconnected nodes by constantly spreading malware. In order to prevent these attacks in their early stages, which includes stopping the spread of malware, it is vital to identify compromised nodes and successfully predict potential attack paths. To this end, this paper proposes a novel system based on an Associated Random Neural Network (ARNN) that simultaneously detects intrusion at the network-level and estimates the network attack graph. In this system, ARNN is trained online to minimize problem-specific multi-task loss so that it identifies compromised network nodes, while the neural network connection weights also estimate the attack path. The performance of the method is calculated using the Kitsune attack dataset, showing that the method achieves a recall rate above 0.95 in estimating the network attack graph, and provides a near-perfect classification of compromised nodes. The ARNN-based system for dynamic and continuous estimation of compromised nodes and network attack graphs, can pave the way for enhancing security measures, and stopping Botnet DDoS attacks from spreading in networked systems.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">An Associated Random Neural Network Detects Intrusions and Estimates Attack Graphs</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Baran Gül</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mert Nakip</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Federated Intrusion Detection for Smart Vehicles</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2026</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE International Conference on Intelligent Transportation Systems (ITSC)</style></SECONDARY_TITLE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Cybersecurity threats endanger networked systems consisting of multiple nodes, such as the Internet of Things (IoT), Supply Chains and Smart Cars, due to the emergence of new types of unknown (zero-day) attacks against vulnerable IoT devices. While earlier research has shown that Machine Learning (ML)-based Intrusion Detection Systems (IDSs) enhance security, the efficacy and practicality of ML-based IDSs often require large training datasets based on private data. This challenge becomes more pronounced in the case of a distributed system, where each node must ensure the confidentiality of local data, yet faces attacks that are similar to those faced by other nodes in the system. Thus, this paper proposes a novel Decentralized Asynchronous IDS with Online Self-Supervised Federated Learning called the iDAF system, that improves overall security by providing collaborative confidentiality-preserving learning among all nodes. iDAF enables self-supervised learning without human intervention or large labelled data, and avoids time-consuming local training sessions. iDAF’s performance is evaluated for DoS and DDoS attacks in two different scenarios of Collaborating IoT Networks and Connected Smart Cars using three online datasets, and compared with five benchmark methods. The results show high intrusion detection accuracy with an acceptable computation time and low communication overhead.&lt;/p&gt;</style></ABSTRACT><title><style face="normal" font="default" size="100%">Federated Intrusion Detection for Smart Vehicles</style></title></RECORD></records></xml>
