An Artificial Immune-Based Distributed Intrusion Detection Model for the Internet of Things

Abstract

Traditional detection technology for network attacks is difficult to adapt the complicated and changeful environment of the Internet of Things (IoT). In the interest of resolving the distributed intrusion detection problem of IoT, this paper proposes an artificial immune-based theory model for distributed intrusion detection in IoT. Artificial immune principles are used to solve the problem of IoT intrusion detection. Antigen, self, non-self and detector in the IoT environment are defined. Good immune mechanisms are simulated. Detector is evolved dynamically to make the proposed model have self-learning and self-adaptation. The outstanding detectors which have accepted training are shared in the whole IoT to adapt the local IoT environment and improve the ability of global intrusion detection in IoT. The proposed model is expected to realize detecting intrusion of IoT in distribution and parallelity.