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A Survey and Comparative Analysis of Data Mining Techniques for Network Intrusion Detection Systems

Author(s): Reema Patel | Amit Thakkar | Amit Ganatra

Journal: International Journal of Soft Computing & Engineering
ISSN 2231-2307

Volume: 2;
Issue: 1;
Start page: 265;
Date: 2012;
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Keywords: Classification | Data Mining | Intrusion Detection System

Despite of growing information technology widely, security has remained one challenging area for computers and networks. In information security, intrusion detection is the act of detecting actions that attempt to compromise the confidentiality, integrity or availability of a resource. Currently many researchers have focused on intrusion detection system based on data mining techniques as an efficient artifice. Data mining is one of the technologies applied to intrusion detection to invent a new pattern from the massive network data as well as to reduce the strain of the manual compilations of the intrusion and normal behavior patterns. This article reviews the current state of art data mining techniques, compares various data mining techniques used to implement an intrusion detection system such as Decision Trees, Artificial Neural Network, Naïve Bayes, Support Vector Machine and K- Nearest Neighbour Algorithm by highlighting advantages and disadvantages of each of the techniques. Finally, a discussion of the future technologies and methodologies which promise to enhance the ability of computer systems to detect intrusion is provided and current research challenges are pointed out in the field of intrusion detection system.

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