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

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.