期刊名称：International Journal of Electronics and Computer Science Engineering
电子版ISSN：2277-1956
出版年度：2013
卷号：2
期号：3
页码：1059-1064
出版社：Buldanshahr : IJECSE
摘要：The traditional Intrusion detection systems have been used long time ago, namely Anomaly-Based detection and Signature-based detection but have many drawbacks that limit their performance. Consequently the main goal of this paper is to use data mining techniques including classification using clustering methods to overpass these defects. This classification will be done by using k-means algorithm. Therefore we have improved k-means to overcome its limits specially the cluster’s number initialization. The experiment results of the work done on KDD’99 dataset shows the performance of the improved k-means in detecting new attacks with more than 90% for Dos and R2L also more than 60% for probe and U2R.
关键词：Intrusion detection; data mining; classification; clustering; k-means; KDD Cup’99.
