Distributed Danger Assessment Model for the Internet of Things Based on Immunology

The Internet of Things (IoT) confronts complicated and changeful security threats. It harms IoT and brings IoT potential danger. However, the research achievements of the danger assessment technology for IoT are rare. To calculate the danger value of IoT with many dispersive sense nodes, the theoretical model of distributed danger assessment for IoT is explored in this paper. The principles and mechanisms of Artificial Immune System (AIS) are introduced into the proposed model. Data packets in IoT are captured in each gateway and converted into antigens in the simulated immune environment. Detectors use self-learning and self-adaptation mechanisms in AIS to evolve themselves to adapt the local IoT environment and detect security threats. The mechanism of antibody density is simulated to reflect the intensity of security threats which are happening. Through the detected security threats and their intensity, the values of IoT property and security threats’ harm are combined to assess the quantitative value of danger for IoT. Theoretical analysis shows that the proposed model is significative of theory and practice.