基于PSO的k-means算法及其在网络入侵检测中的应用  

PSO-based k-means Algorithm and its Application in Network Intrusion Detection System

作　　者：傅涛[1] 孙亚民[1] 

机构地区：[1]南京理工大学计算机与技术学院,南京210094

出　　处：《计算机科学》2011年第5期54-55,73,共3页Computer Science

基　　金：江苏省产业技术研究与开发基金;苏发改高技发[2008]106号资助

摘　　要：在传统k-means算法中,初始聚类中心随机选择,聚类结果随初始聚类中心的不同而波动,从而导致聚类结果不稳定。提出的PSO-based k-means算法使用PSO算法优化生成初始聚类中心,得到的聚类结果全局最优,不会陷入局部最优解。实验结果表明,将PSO-based k-means算法用于入侵检测系统的规则挖掘处理模块,其入侵检测率明显高于传统k-means算法,而误报率则大大低于后者。显然,PSO-based k-means算法可有效提高网络入侵检测系统的性能。In the traditional k-means algorithm,the initial cluster center is chosen randomly,clustering result varies from the initial cluster center,and clustering result is unstable.The PSO-based k-means algorithm was proposed in the paper.The PSO optimization algorithm generates the initial cluster center.The clustering result is global optimal and doesn＇t fall into local optimal solution.Experimental results show that the intrusion detection rate is significantly higher than the traditional k-means algorithm and its false positive rate is largely lower than the latter by applying the PSO-based k-means algorithm to the rule mining module of intrusion detection system.Obviously,the PSO-based k-means algorithm can improve the performance of network intrusion detection system effectively.

关 键 词：PSO-basedk-means 优化聚类 入侵检测 检测率 误报率 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]