期刊名称：International Journal of Engineering and Computer Science
印刷版ISSN：2319-7242
出版年度：2015
卷号：4
期号：12
页码：15281-15286
DOI：10.18535/Ijecs/v4i12.33
出版社：IJECS
摘要：With the increasing of network attacks, network information security has become an issue ofglobal concern. The problem with the mainstream intrusion detection system is the huge number of alarminformation, it has high false positive rate. This paper presents a data mining technology to reduce falsepositive rate and improve the accuracy of detection. The technique is unsupervised clustering method basedon hybrid ANT algorithm, it can discover clusters of intruders’ behavior without prior knowledge. we useK-means algorithm to improve the convergence speed of the ANT clustering. Experimental results show thatour proposed approach has higher detection rate and lower false alarm rate
关键词：intrusion detection; alarms filtering; ant clustering; false alarms
