文章基本信息

标题：A Comprehensive Study in Data Mining Frameworks for Intrusion Detection
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作者：R.Venkatesan ; R. Ganesan ; A. Arul Lawrence Selvakumar 等
期刊名称：International Journal of Advanced Computer Research
印刷版ISSN：2249-7277
电子版ISSN：2277-7970
出版年度：2012
卷号：2
期号：7
页码：29-34
出版社：Association of Computer Communication Education for National Triumph (ACCENT)
摘要：Intrusions are the activities that violate the security policy of system. Intrusion Detection is the process used to identify intrusions. Network security is to be considered as a major issue in recent years, since the computer network keeps on expanding every day. An Intrusion Detection System (IDS) is a system for detecting intrusions and reporting to the authority or to the network administration. Data mining techniques have been successfully applied in many fields like Network Management, Education, Science, Business, Manufacturing, Process control, and Fraud Detection. Data Mining for IDS is the technique which can be used mainly to identify unknown attacks and to raise alarms when security violations are detected. The purpose of this survey paper is to describe the methods/ techniques which are being used for Intrusion Detection based on Data mining concepts and the designed frame works for the same. We are also going to review the related works for intrusion detection.
关键词：Data Mining; Intrusion Detection System (IDS); Network Security; Misuse Detection; Anomaly Detection; Classification; Clustering; MADAM ID; ADAM; JAM.