Network Layer Security Detection Model of Internet of Things Based on Immune System

Abstract

The traditional network security detection model use attack samples of known types to train the security detection model offline. Although it has a high detection rate for the known attack types, it cannot identify the new attack types on the network layer. At present, detection system has disadvantages of slow speed of system construction and high cost of model update. Facing increasingly expanding network and endless attacks, the detection system lacks self-adaptability and expansibility. In this paper, the integrated use of immunology theory and complex adaptive system theory and computational experiment technology, based on Internet network layer security detection model of the immune system, existing Internet network layer security, the new type of attack occurred in the process of on-line detection of immune learning, realize the dynamic extension of network security detection model, and through calculating experiment and example analysis verify the scientific nature and feasibility of the model.