改进k-means算法的网络数据库入侵检测

Improved K-means Algorithm Network Database Intrusion Detection

提出改进的k-means算法,加入过滤优化功能,通过簇候选集合中攻击簇的数目优化,删除掉非最优聚类数据集合中的攻击数据,生产最优簇,提高后期网络数据库入侵检测的时效性,降低漏检率.实验结果表明,本文的方法能够优化聚类后生成攻击簇的数目的数目,为网络数据量入侵检测提供便利,提高了检测的准确性,降低了漏检率.

This paper proposed the improvement k-means algorithm,join filter optimization function through the cluster set the number of candidates clusters,deleted the optimal cluster the data in the data set,the optimal cluster,improve production later network database intrusion detection,reduce the efficiency of the miss rate.The experiment results show that the method can optimize the clustering to create the number of clusters,for the number of network data quantity intrusion detection provides convenience,improve the detection accuracy,and to reduce the miss rate.