Research on Immune Based Adaptive Intrusion Detection System Model

Intrusion detection systems (IDSs) are increasingly a key part of systems defense. Various approaches to intrusion detection are currently being used, but they are relatively ineffective. Recently applying artificial intelligence, machine learning and data mining techniques to IDS are increasing. Artificial intelligence plays a driving role in security services. This paper proposes an Immune based adaptive intrusion detection system model (IAIDSM). Analyzing the training data obtaining from Internet, the self behavior set and nonself behavior set can be obtained by the partitional clustering algorithm, then it extracts Self and nonself pattern sets from these two behavior sets by association rules and sequential patterns mining. The self and nonself sets can update automatically and constantly online. So IAIDSM improves the ability of detecting new type intrusions and the adaptability of the system.