Real-Time Cyberattack Detection with Offline and Online Learning. (arXiv:2303.11760v1 [cs.CR])

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 …

algorithm algorithms attacks auto cyberattack detect detection flow horizon learn network neural network novel online learning project random testing traffic