Design of a new distributed model for Intrusion Detection System based on Artificial Immune System

Currently Intrusion detection systems have grown to be an ordinary component of network security infrastructure. With mounting global network connectivity, the issue of intrusion has achieved importance, promoting active research on efficient Intrusion Detection Systems (IDS). Artificial Immune System (AIS) is a new bio-inspired model which is applied for solving various problems in the field of information security. The unique features AIS encourage the researchers to employ this techniques in variety of applications and especially in intrusion detection systems. Proper IDS design is essential to improve the performance of the IDS. The centralized design of this IDS has disadvantage of central processing for massive processes for each packets passing trough network. In this paper we proposed a distributed multi-layerd framework to enhance the detection performance and efficiency of this IDS. In our design the genetic algorithm is used for enhancing the secondary immune response. The fundamental design of our proposed AIS based IDS consists of 2 main components: IDS central engine and detection sensors. Each of these components is composed of some agents which correlate with each other in order to detect the anomalies and intrusions. Our design goal is to decrease the detection time for each connection by distributing the detectors to each host.