Leveraging edge learning and game theory for intrusion detection in Internet of things

With the commercialization of 5G and the development of 6G, more and more Internet of things (IoT) devices are linked to the novel cyber-physical system (CPS) to support intelligent decision making. However, the highly decentralized and heterogeneous IoT devices face potential threats that may mislead the CPS. Traditional intrusion detection solutions cannot protect the privacy of IoT devices, and they have to deal with the single point of failure, which prevents these solutions from being deploying in IoT scenarios. The edge learning and game theory based intrusion detection for IoT was proposed. Firstly, an edge learning based intrusion detection framework was proposed to detect potential threats in IoT. Moreover, a multi-leader multi-follower game was employed to motivate trusted parameter servers and edge devices to participate in the edge learning process. Experiments and evaluations show the security and effectiveness of the proposed intrusion detection framework.