An Improved Artificial Immune Algorithm Applied In Intrusion Detection System

In recent years, with the increasing popularity of the Internet, how to build a safe, efficient and stable network protection system has been paid more and more attention. Now, many biological principles have been applied to network security.There are a lot of similarities between computer security system and biological immune system. Both of them need to maintain the stability of system in the environment which changes continually. So, we can apply the principle of biological immune system to computer security system. This thesis mainly applied the Artificial Immune Algorithm into the Intrusion Detection System to protect the security of network.This thesis analyzed the classical Artificial Immune Algorithm, improved the nuclear part of Artificial Immune Algorithm—Negative Selection Algorithm, and combined the negative selection algorithm with the clonal selection algorithm, enhanced the performance of intrusion detection system, reduced the error rate. The main work of this thesis has three parts as below:(1) Partitioned the match string, set different coefficient for each section, thus eliminated the problem caused by the r-continual position match algorithm which has the constant match probability, and reduced the missing rate of intrusion detection system;(2) Combined the Negative Selection algorithm with the Clonal Selection algorithm. This will increase the reproduction, selection and intersection process into the generating process of detection. Thus the missing rate will be reduced;(3) Compared and analyzed the different parameters, including the section number, threshold value and r-continual parameter.