文章基本信息

标题：Clustering Based Network Intrusion Detection Using Kdd Train 20 Percent
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作者：Poonam Dabas ; Rashmi Chaudhary
期刊名称：International Journal of Advanced Research In Computer Science and Software Engineering
印刷版ISSN：2277-6451
电子版ISSN：2277-128X
出版年度：2013
卷号：3
期号：6
出版社：S.S. Mishra
摘要：In this Paper a clustering algorithm is proposed to work on network intrusion data. The algorithm is experimented with KDD Train 20 percent dataset and found satisfactory results. We perform clustering to group training data points into clusters, from which we select some clusters as normal and known-attack profile according to certain criterion. For those training data excluded from the profile, we use them to build a specific classifier. During the testing stage, we utilize influence based classification algorithm to classify network behaviors. In the algorithm, an influence function quantifies the influence of an object. The experiments on the KDD Train 20 percent Intrusion Detection Data Set demonstrate the detection performance and the effectiveness of our ID approach In this Dissertation, Intrusion Detection Systems detect the malicious attack which generally includes theft of information or data. It is found from the studies that clustering based intrusion detection methods may be helpful in detecting unknown attack patterns compared to traditional intrusion detection systems
关键词：Intrusion Detection system; Cluster; KDD Train 20 percent; False rate; True Rate; Normal; ;Anomaly;Condition