Research On Matching Algorithm Of Artificial Immune System

Artificial immune system is an intelligent system which is constructed based on biological immune principle, and it could be used to solve problems in the field of computer security. Negative selection algorithm is one of the core algorithms of artificial immune system, and is widely used in virus detection, network intrusion detection system. But it is always very difficult for a intrusion detection system based on negative selection to get a low missing rate and good productivity of detectors. Matching algorithm is closely related to a system's missing rate and productivity of detectors, as a result, to research a new matching algorithm to improve the system's performance has important significance.The paper interpreted the principle of some important matching algorithm, and tested the performance of the matching algorithms by simulation experiments. By analyzing the data, how the parameters affect the performance of matching algorithm is clear to see. This thesis also analyzed the principle of matching algorithm by mathematical models, elaborated the concepts of undetected and false report, and indicated that a good matching algorithm should be able to provide as many probability of matching success as possible. Based on the theoretical analysis, an improved matching algorithm is introduced. The improved matching algorithm could get better flexibility and adaptability with its two threshold, so it help the system to get low missing rate. The paper also proved its performance by simulation experiments testing.At last, according to the experiments and theoretical analysis in the research, some recommendations are given on how to improve performance of system when apply to practice.