INFORMATION CHANGE THE WORLD

International Journal of Computer Network and Information Security(IJCNIS)

ISSN: 2074-9090 (Print), ISSN: 2074-9104 (Online)

Published By: MECS Press

IJCNIS Vol.6, No.7, Jun. 2014

Feature Selection for Modeling Intrusion Detection

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Author(s)

Virendra Barot, Sameer Singh Chauhan, Bhavesh Patel

Index Terms

Feature selection;network intrusion detection system;decision table majority;naive Bayesian classification

Abstract

Feature selection is always beneficial to the field like Intrusion Detection, where vast amount of features extracted from network traffic needs to be analysed. All features extracted are not informative and some of them are redundant also. We investigated the performance of three feature selection algorithms Chi-square, Information Gain based and Correlation based with Naive Bayes (NB) and Decision Table Majority Classifier. Empirical results show that significant feature selection can help to design an IDS that is lightweight, efficient and effective for real world detection systems.

Cite This Paper

Virendra Barot, Sameer Singh Chauhan, Bhavesh Patel,"Feature Selection for Modeling Intrusion Detection", IJCNIS, vol.6, no.7, pp.56-62, 2014. DOI: 10.5815/ijcnis.2014.07.08

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