Using Self-Organizing Maps with Learning Classifier System for Intrusion Detection

Authors:
Kreangsak Tamee;Pornthep Rojanavasu;Sonchai Udomthanapong;Ouen Pinngern
Affiliations:
Department of Computer Engineering, Faculty of Engineering, Research Center for Communication and Information Technology (ReCCIT), King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thail ...;Department of Computer Engineering, Faculty of Engineering, Research Center for Communication and Information Technology (ReCCIT), King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thail ...;Department of Computer Engineering, Faculty of Engineering, Research Center for Communication and Information Technology (ReCCIT), King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thail ...;Department of Computer Science, Faculty of Science, Ramkhamhaeng University, Bangkok, Thailand 10240
Venue:
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Year:
2008

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Abstract

Learning Classifier Systems (LCS) have previously been shown to have application in Intrusion Detection. This paper extends work in the area by applying the Self-Organizing Map (SOM) for creating the new input string by 2-bit encoding rely on degree of deviation of normal behaviour. The performance of systems is investigated under an FTP-only dataset. It is shown that the proposed system is able to perform significantly better than the conventional XCS, modified XCS and twelve ML algorithms.