文章基本信息

标题：Some Studies in Intrusion Detection using Data Mining Techniques
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作者：Inadyuti Dutt ; Dr. Samarjeet Borah
期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2015
卷号：4
期号：7
页码：5500
DOI：10.15680/IJIRSET.2015.0407090
出版社：S&S Publications
摘要：This paper surveys the present data mining techniques used in detecting intrusions in a computernetworks. The basic objective of this survey paper is to review the current methodologies of data mining techniquebeing used in Intrusion Detection System. The paper focuses on the usage of various methodologies of data miningtechnique like clustering, classification and other data mining rules. The results suggest that classification methodologyis being widely used for solving intruder-based problems and Support Vector Machine (SVM) remains popular withinthis arena, for the researchers. Similarly, in clustering technique, statistical-based, conditional probability i.e. Bayesianclustering, and its native are used for categorizing attack from a non-attack. Even if these methodologies score well inintrusion detection, the hybrid models introduced generate good performances in lowering false alarms.
关键词：Intrusion Detection System (IDS); Data Mining; Support Vector Machine (SVM); Clustering;Classification; Decision tree; C4.5