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A Comprehensive Study in Data Mining Frameworks for Intrusion Detection

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Author(s): R.Venkatesan, R. Ganesan, A. Arul Lawrence Selvakumar

Journal: International Journal of Advanced Computer Research
ISSN 2249-7277

Volume: 2;
Issue: 7;
Start page: 29;
Date: 2012;
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Keywords: Data Mining | Intrusion Detection System (IDS) | Network Security | Misuse Detection | Anomaly Detection | Classification | Clustering | MADAM ID | ADAM | JAM

ABSTRACT
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.
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