Using self-organizing maps with learning classifier system for intrusion detection

Tamee, Kreangsak; Rojanavasu, Pornthep; Udomthanapong, Sonchai; Pinngern, Ouen

doi:10.1007/978-3-540-89197-0_109

Using self-organizing maps with learning classifier system for intrusion detection

Author(s)

Tamee, Kreangsak

Rojanavasu, Pornthep

Udomthanapong, Sonchai

Pinngern, Ouen

Date Issued

December 1, 2008

Type

Conference Paper

DOI

10.1007/978-3-540-89197-0_109

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. © 2008 Springer Berlin Heidelberg.

Citation

Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 5351 LNAI, 1071-1076, 2008

Subjects

Intrusion detection

LCS

Self-organizing map