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

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.