Intrusion detection using emergent self-organizing maps
Paper i proceeding, 2006

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. © Springer-Verlag Berlin Heidelberg 2006.

Classification

Self-Organising Maps

Intrusion Detection

Författare

Aikaterini Mitrokotsa

Chalmers, Data- och informationsteknik

C. Douligeris

Proceedings of the 4th Helenic Conference on AI (SETN 2006)

Vol. 3955

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Data- och informationsvetenskap

DOI

10.1007/11752912_68

ISBN

354034117X

Mer information

Skapat

2017-10-08