Real-Time Cyberattack Detection with Offline and Online Learning

TytułReal-Time Cyberattack Detection with Offline and Online Learning
Publication TypeConference Paper
Rok publikacji2023
AutorzyGelenbe E, Nakip M
Conference NameIEEE International Symposium on Local and Metropolitan Area Networks
Conference LocationLondon, United Kingdom
Słowa kluczoweAttack Detection, Auto-Associative Random Neural Network, Cybersecurity, Internet of Things (IoT), Random Neural Network

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,


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Data aktualizacji: 27/07/2023 - 10:22; autor zmian: Mert Nakip (