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Journal of Zhejiang University SCIENCE A 2004 Vol.5 No.9 P.1076~1086

http://doi.org/10.1631/jzus.2004.1076


Intrusion detection using rough set classification


Author(s):  ZHANG Lian-hua, ZHANG Guan-hua, YU Lang, ZHANG Jie, BAI Ying-cai

Affiliation(s):  Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai 200030, China; more

Corresponding email(s):   a000309035@21cn.com

Key Words:  Intrusion detection, Rough set classification, Support vector machine, Genetic algorithm


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ZHANG Lian-hua, ZHANG Guan-hua, YU Lang, ZHANG Jie, BAI Ying-cai. Intrusion detection using rough set classification[J]. Journal of Zhejiang University Science A, 2004, 5(9): 1076~1086.

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author="ZHANG Lian-hua, ZHANG Guan-hua, YU Lang, ZHANG Jie, BAI Ying-cai",
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Abstract: 
Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learning algorithm, is used to rank the features extracted for detecting intrusions and generate intrusion detection models. Feature ranking is a very critical step when building the model. RSC performs feature ranking before generating rules, and converts the feature ranking to minimal hitting set problem addressed by using genetic algorithm (GA). This is done in classical approaches using support vector machine (SVM) by executing many iterations, each of which removes one useless feature. Compared with those methods, our method can avoid many iterations. In addition, a hybrid genetic algorithm is proposed to increase the convergence speed and decrease the training time of RSC. The models generated by RSC take the form of “IF-THEN” rules, which have the advantage of explication. Tests and comparison of RSC with SVM on DARPA benchmark data showed that for Probe and DoS attacks both RSC and SVM yielded highly accurate results (greater than 99% accuracy on testing set).

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Reference

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[9] Pawlak, Z., 1982. Rough sets. International Journal of Computer and Information Sciences, 11:341-356.

[10] Srinivas, M., Sung, A., 2002. Feature Ranking and Selection for Intrusion Detection. Proceedings of the International Conference on Information and Knowledge Engineering.

[11] Wang, G.Y., eds, 2001. Rough Set Theory and Knowledge Acquistion. Xi’an Jiaotong University Press, Xi’an (in Chinese).

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