Intrusion detection using emergent self-organizing maps
Paper in proceedings, 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

Author

Aikaterini Mitrokotsa

Chalmers, Computer Science and Engineering (Chalmers)

C. Douligeris

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

Vol. 3955

Areas of Advance

Information and Communication Technology

Subject Categories

Computer and Information Science

DOI

10.1007/11752912_68

ISBN

354034117X

More information

Created

10/8/2017