Intrusion Detection Algorithm Based On Danger Theory Study

Intrusion detection is an important task in the field of information security. As an active safety technique of information security, the IDS (Intrusion Detection System) can improve the secure ability of system and reduce the foreign menace to the system.Building the IIDS (intelligent IDS) is the linchpin of enhancing the capability of IDS. AIS (Artificial Immune System) simulates the strongpoint of BIS(Biological Immune System) such as tolerance, self-adaptive capacity, diversity, with good intelligence, can fetch up the disadvantage of IDS such as low True Positive Rate, high False Positive Rate and False Negative Rate, and has a good ability of detecting the unknown intrusion behavior.Over the last decade, Immune Danger Theory has become popular among immunologists. It does not take the viewpoint of self-nonself, but the absence or presence of danger signal. Although it does not get admitted widely, it can be extended to Artificial Immune System as a kind of simulation towards Biological Immune System.In this paper, the main work is as follows:Firstly, background, definition and functions of intrusion detection are introduced in this paper. Some intrusion detection methods, which are often used, are introduced.Secondly, the danger theory is very complicated and obscure, we simplify and modify it according to intrusion detection realm for the sake of better application.Thirdly, implements the algorithm by C++ language under Suse Linux10 operating system, and designs the simulative experiment of IDS model based on Immune Danger Theory with KDD data set as the intrusion data set.Fourthly, proposes suggestion on how to apply our algorithm to the real network environment .