Using genetic feature selection for improving cyber attack detection rate

Authors:
Chi Hoon Lee;Doo Hyung Lee;Jin Wook Chung
Affiliations:
School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea;School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea;School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea
Venue:
ACST'07 Proceedings of the third conference on IASTED International Conference: Advances in Computer Science and Technology
Year:
2007

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Abstract

As Internet becomes an essential tool for all kinds of business transactions, the issue for detecting network intrusion has received greater attention. In this paper, we suggest a novel method based on a genetic optimization that can improve the detection rate for attack patterns without a loss due to false-positive error rate. We focus on selecting a robust feature subset by designing a multicriteria optimization procedure. During the evaluation phase, the performance of proposed approach is contrasted against one of the state-of-the-art feature selection methods using a k nearest neighbor classifier. Experimental results show that the proposed approach is remarkably effective than using the full feature set.