Multi-Agent Distributed Intrusion Detection System Model Based on BP Neural Network

Abstract

On the basis of analyzing the existing intrusion detection system (IDS) based on agent, this paper proposed a multi-agent distributed IDS (DIDS) model based on BP neural network. This model adopted the modes of distributed detection and distributed response. Each agent was independence relatively. And this model analyzed the functional design of each agent and central console. Meanwhile, to improve the performance of the system, an improved error back-propagation algorithm was designed, which could improve the detection accuracy of the system by using its good learning ability. In addition, the dynamic election algorithm and collaborative algorithm were analyzed preliminarily. Experiments proved that the system could complete the intrusion detection tasks by making full use of various resources collaboratively, and thus the detection speed and accuracy of the system could be improved.