01179nas a2200133   4500000000100000000000100001008004100002260003300043100001700076700001500093245006900108856005000177520081800227       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>