Intrusion detection using PCASOM neural networks

Authors:
Guisong Liu;Zhang Yi
Affiliations:
Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China;Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

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Abstract

This paper proposes a method to detect network intrusions by using the PCASOM (principal components analysis and self-organizing map) neural networks. A modified unsupervised learning algorithm which is more suitable for intrusion detection is presented. Experiments are carried out to illustrate the performance of the proposed method by using DARPA 1998 evaluation data sets. It shows that the proposed method can cluster the network connections into proper clusters with high detection rate and relatively low false alarm rate.