基于粒子群优化的聚类入侵检测算法  

Anomaly detection for clustering algorithm based on particle swarm optimization

作　　者：李永忠[1] 杨鸽[1] 徐静[1] 赵博[1] 孙彦[1] 

机构地区：[1]江苏科技大学计算机科学与工程学院,江苏镇江212003

出　　处：《江苏科技大学学报（自然科学版）》2009年第1期51-55,共5页Journal of Jiangsu University of Science and Technology:Natural Science Edition

基　　金：江苏省高校自然科学基金资助项目(2005DX006J)

摘　　要：为了在入侵检测中有效地克服传统的K均值算法易陷入局部极小值的缺点,使算法具有较好的全局收敛性,将粒子群优化算法应用于入侵检测,给出了基于粒子群优化的K均值聚类算法.通过理论分析及实验,验证了基于粒子群优化K均值聚类算法的有效性.对KDD CUP99数据集仿真,实验结果表明,该算法在入侵检测中能获得理想的检测率和误检率.As an effective method, K-mean clustering algorithm has been applied to the intrusion detection, but it is local optimal solution rather than global optimal solution. Particle swarm optimization （PSO） algorithm is a kind of swarm intelligence ones, and it has a good global search capabilities. K-means algorithm based on particle swarm optimization was proposed in this paper. The analysis and experiment show this algorithm may avoid local optima, and has good global convergence. Also, the algorithm is effective. Furthermore, KDD CUP99 data set is implemented to evaluate the proposed method. The results show the outstanding performance of the proposed method.

关 键 词：粒子群优化 K均值算法 入侵检测 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]