Intelligent Detection of Major Network Attacks Using Feature Selection Methods

Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 131)

Abstract

Intrusion detection system (IDS) detects an illegal exploitation of computer systems. In intrusion detection systems, feature selection plays an important role in a sense of improving classification performance and reducing the computational complexity. In this paper, we focus on improving identification of major network attacks like DoS, R2L and Probe using various feature selection techniques (IG, CHI2 and OCFS). This research work explored the possibility of employing a variety of classifiers, but limited to J48, Naive Bayes and AdaBoost. Empirical evaluations were completed based on a standard network intrusion data set (KDDCUP99). The Experimental results show that the feature selection approach gives considerable increase of performance in detecting network intrusions as compared to normal approach.

Keywords

Feature Selection Intrusion Detection System Data Mining 

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Copyright information

© Springer India Pvt. Ltd. 2012

Authors and Affiliations

  1. 1.Department of Information TechnologyPune Institute of Computer TechnologyPuneIndia
  2. 2.Department of Computer Engineering and Information TechnologyCollege of Engineering PunePuneIndia

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