Feature subset selection using binary gravitational search algorithm for intrusion detection system

Authors:
Amir Rajabi Behjat;Aida Mustapha;Hossein Nezamabadi---pour;Md. Nasir Sulaiman;Norwati Mustapha
Affiliations:
Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor, Malaysia;Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor, Malaysia;Department of Electrical Engineering, Shahid Bahonar University of Kerman, Iran;Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor, Malaysia;Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
Venue:
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
Year:
2013

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Abstract

Due to control different infrastructures of networked computers in cyber security, intrusion detection system has been an important task essentially. Today, an effective intrusion detection system utilizes computational methods as machine learning techniques to improve detection rate with lowest false positive rate; however large number of irrelevant features as an optimization problem decrease this rate. This study using Binary Search Gravitational Algorithm (BGSA) as a feature selection method decreases irrelevant features in KDD 99 intrusion detection data set in order to improve Multi-layer perceptron performance. Results show that significant and relevant features increase performance of intrusion detection system near to 100% with lowest computational cost.