Real-valued multi-area self set optimization in immunity-based network intrusion detection system  

作　　者：Zhang Fengbin Xi Liang Wang Shengwen 

机构地区：[1]College of Computer Science and Technology,Harbin University of Science and Technology,Harbin 150080,P.R.China

出　　处：《High Technology Letters》2012年第1期1-6,共6页高技术通讯（英文版）

基　　金：Supported by the National Natural Science Foundation of China （No. 60671049, 61172168）and Graduate Innovation Project of Heilongjiang （No. YJSCX2011-034HLI）

摘　　要：The real-valued self set in immunity-based network intrusion detection system （INIDS） has some defects： multi-area and overlapping, which are ignored before. The detectors generated by this kind of self set may have the problem of boundary holes between self and nonself regions, and the generation efficiency is low, so that, the self set needs to be optimized before generation stage. This paper proposes a self set optimization algorithm which uses the modified clustering algorithm and Gaussian distribution theory. The clustering deals with multi-area and the Gaussian distribution deals with the overlapping. The algorithm was tested by Iris data and real network data, and the results show that the optimized self set can solve the problem of boundary holes, increase the efficiency of detector generation effectively, and improve the system＇s detection rate.

关 键 词：immunity-based network intrusion detection system （NIDS） real-valued self set OPTIMIZATION 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术] O242.23[自动化与计算机技术—计算机科学与技术]