Cited By
View all- Wu SBanzhaf W(2010)ReviewApplied Soft Computing10.1016/j.asoc.2009.06.01910:1(1-35)Online publication date: 1-Jan-2010
Intrusion detection using emergent self-organizing maps
Pages 559 - 562
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.
References
[1]
S. Haykin, "Neural Networks: A comprehensive Foundation", Prentice- Hall, USA, 2nd edit.
[2]
A. Ultsch, "Data Mining and Knowledge Discovery with Emergent SOFMs for Multivariate Time Series", In Kohonen Maps, (1999), pp. 33-46.
[3]
A. Ultsch, "Maps for Visualization of High-Dimensional Data Spaces", Proc. WSOM, Kyushu, Japan, (2003), pp. 225-230.
[4]
S. Mukkamala, A.H. Sung, "Identifying Significant Features for Network Forensic Analysis Using Artificial Intelligent Techniques", International Journal of Digital Evidence,2003(1)4.
[5]
Databionic ESOM Tools, http://databionic- esom.sourceforge.net/devel.html.
[6]
The Third International Knowledge Discovery and Data Mining Tools Competition, May 2002. http://kdd.ics.uci.edu/databases/kddcup99.kddcup99.html
- Intrusion detection using emergent self-organizing maps
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Published In
May 2006
608 pages
ISBN:354034117X
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 18 May 2006
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View all- Wu SBanzhaf W(2010)ReviewApplied Soft Computing10.1016/j.asoc.2009.06.01910:1(1-35)Online publication date: 1-Jan-2010