基于CS算法优化的模糊C均值聚类入侵检测

Intrusion detection with the fuzzy c-means clustering algorithm optimized by the CS

针对模糊C均值聚类算法受初始聚类中心影响过大以及易于陷入局部极值的问题,采用具有Levy flight模式且具有很强全局搜索能力的布谷鸟搜索算法,对模糊C均值聚类算法初始聚类中心进行优化,并把优化后的模糊C均值聚类算法应用于网络入侵检测。实验结果显示,经过优化后的模糊C均值聚类算法具有较好的运行速度和聚类效果,对入侵行为的检测效果良好。

The Fuzzy C-means clustering algorithm is influenced by the initial cluster center and is easy to fall into local extremum. The cuckoo search algorithm（CS）, which has a levy flight mode and a strong global search capability, can optimize the initial cluster centers of the fuzzy C-means clustering algorithm. And then the optimized algorithm is applied to network intrusion detection. Experimental results show that the optimized algorithm has better operating speeds and clustering effect and has good effect to intrusion detection.