DECENTRALIZED APPROACH TO INTRUSION DETECTION IN DYNAMIC NETWORKS OF THE INTERNET OF THINGS BASING ON MULTI-AGENT REINFORCEMENT LEARNING AND INTER-AGENT COMMUNICATION
M. O. Kalinin, E. I. Tkacheva Peter the Great St. Petersburg Polytechnic University (SPbPU)
Annotation: The paper proposes a multi-agent reinforcement learning technology for intrusion detection in the Internet of Things. Three models of a multi-agent intrusion detection system have been implemented – a decentralized system, a system with the transmission of forecasts, a system with the transmission of observations. The obtained experimental results have been compared with the open intrusion detection system Suricata. It has been demonstrated that the proposed architectures of multi-agent systems are free from the weaknesses found in the usual solutions.
Keywords: agent, decentralized system, internet of things, greedy algorithm, cybersecurity, machine learning, multi-agent reinforcement learning, intrusion detection, observation data transferring, prediction data transferring, DQN.