期刊名称：International Journal of Data Mining & Knowledge Management Process
印刷版ISSN：2231-007X
电子版ISSN：2230-9608
出版年度：2014
卷号：4
期号：2
页码：29
DOI：10.5121/ijdkp.2014.4203
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Recent advances in technology have made our work easier compare to earlier times. Computer network isgrowing day by day but while discussing about the security of computers and networks it has always been amajor concerns for organizations varying from smaller to larger enterprises. It is true that organizationsare aware of the possible threats and attacks so they always prepare for the safer side but due to someloopholes attackers are able to make attacks.Intrusion detection is one of the major fields of research and researchers are trying to find new algorithmsfor detecting intrusions. Clustering techniques of data mining is an interested area of research for detectingpossible intrusions and attacks. This paper presents a new clustering approach for anomaly intrusiondetection by using the approach of K-medoids method of clustering and its certain modifications. Theproposed algorithm is able to achieve high detection rate and overcomes the disadvantages of K-meansalgorithm.
关键词：Clustering; data mining; intrusion detection; network security
