Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection

Classification of intrusion attacks and normal network traffic is a challenging and critical problem in pattern recognition and network security. In this paper, we present a novel intrusion detection approach to extract both accurate and interpretable fuzzy IF–THEN rules from network traffic data fo...

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Published in:	Pattern recognition Vol. 40; no. 9; pp. 2373 - 2391
Main Authors:	Tsang, Chi-Ho, Kwong, Sam, Wang, Hanli
Format:	Journal Article
Language:	English
Published:	Oxford Elsevier Ltd 01.09.2007 Elsevier Science
Subjects:	Applied sciences Exact sciences and technology Feature selection Fuzzy classifier Genetic algorithms Information, signal and communications theory Intrusion detection Miscellaneous Multi-objective optimization Pattern recognition Signal and communications theory Signal processing Signal representation. Spectral analysis Signal, noise Telecommunications and information theory Feature selection Multi-objective optimization Fuzzy classifier Intrusion detection Genetic algorithms Performance evaluation Automatic classification Expert system Multiobjective programming Intruder detector Remote sensing Accuracy Data transmission network Safety Fuzzy system Teletraffic Pattern recognition Signal classification False alarm rate Multiagent system Surveillance Genetic algorithm Signal processing Feature extraction Remote supervision
ISSN:	0031-3203, 1873-5142
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Abstract	Classification of intrusion attacks and normal network traffic is a challenging and critical problem in pattern recognition and network security. In this paper, we present a novel intrusion detection approach to extract both accurate and interpretable fuzzy IF–THEN rules from network traffic data for classification. The proposed fuzzy rule-based system is evolved from an agent-based evolutionary framework and multi-objective optimization. In addition, the proposed system can also act as a genetic feature selection wrapper to search for an optimal feature subset for dimensionality reduction. To evaluate the classification and feature selection performance of our approach, it is compared with some well-known classifiers as well as feature selection filters and wrappers. The extensive experimental results on the KDD-Cup99 intrusion detection benchmark data set demonstrate that the proposed approach produces interpretable fuzzy systems, and outperforms other classifiers and wrappers by providing the highest detection accuracy for intrusion attacks and low false alarm rate for normal network traffic with minimized number of features.
AbstractList	Classification of intrusion attacks and normal network traffic is a challenging and critical problem in pattern recognition and network security. In this paper, we present a novel intrusion detection approach to extract both accurate and interpretable fuzzy IF–THEN rules from network traffic data for classification. The proposed fuzzy rule-based system is evolved from an agent-based evolutionary framework and multi-objective optimization. In addition, the proposed system can also act as a genetic feature selection wrapper to search for an optimal feature subset for dimensionality reduction. To evaluate the classification and feature selection performance of our approach, it is compared with some well-known classifiers as well as feature selection filters and wrappers. The extensive experimental results on the KDD-Cup99 intrusion detection benchmark data set demonstrate that the proposed approach produces interpretable fuzzy systems, and outperforms other classifiers and wrappers by providing the highest detection accuracy for intrusion attacks and low false alarm rate for normal network traffic with minimized number of features.
Author	Wang, Hanli Kwong, Sam Tsang, Chi-Ho
Author_xml	– sequence: 1 givenname: Chi-Ho surname: Tsang fullname: Tsang, Chi-Ho – sequence: 2 givenname: Sam surname: Kwong fullname: Kwong, Sam email: cssamk@cityu.edu.hk – sequence: 3 givenname: Hanli surname: Wang fullname: Wang, Hanli
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Keywords	Feature selection Multi-objective optimization Fuzzy classifier Intrusion detection Genetic algorithms Performance evaluation Automatic classification Expert system Multiobjective programming Intruder detector Remote sensing Accuracy Data transmission network Safety Fuzzy system Teletraffic Pattern recognition Signal classification False alarm rate Multiagent system Surveillance Genetic algorithm Signal processing Feature extraction Remote supervision
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Snippet	Classification of intrusion attacks and normal network traffic is a challenging and critical problem in pattern recognition and network security. In this...
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SubjectTerms	Applied sciences Exact sciences and technology Feature selection Fuzzy classifier Genetic algorithms Information, signal and communications theory Intrusion detection Miscellaneous Multi-objective optimization Pattern recognition Signal and communications theory Signal processing Signal representation. Spectral analysis Signal, noise Telecommunications and information theory
Title	Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection
URI	https://dx.doi.org/10.1016/j.patcog.2006.12.009
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