Intrusion detection using emergent self-organizing maps

Authors:
Aikaterini Mitrokotsa;Christos Douligeris
Affiliations:
Department of Informatics, University of Piraeus, Piraeus, Greece;Department of Informatics, University of Piraeus, Piraeus, Greece
Venue:
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
Year:
2006

Citing 1
Cited 1

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Applied Soft Computing

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Abstract

In this paper, we analyze the potential of using Emergent Self-Organizing Maps (ESOMs) based on Kohonen Self –Organizing maps in order to detect intrusive behaviours. The proposed approach combines machine learning and information visualization techniques to analyze network traffic and is based on classifying “normal” versus “abnormal” traffic. The results are promising as they show the ability of eSOMs to classify normal against abnormal behaviour regarding false alarms and detection probabilities.