01166nas a2200121   4500000000100000008004100001260003300042100001700075700001500092245006900107856005000176520081800226       2023                            d  bIEEEaLondon, United Kingdom1 aErol Gelenbe1 aMert Nakip00aReal-Time Cyberattack Detection with Offline and Online Learning  uhttps://ieeexplore.ieee.org/document/101898123 a<p>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,</p>