Intrusion detection based on dynamic self-organizing map neural network clustering

Abstract

An approach to network intrusion detection is investigated, based on dynamic self-organizing maps (DSOM) neural network clustering. The basic idea of the method is to produce the cluster by DSOM. With the classified data instances, anomaly data clusters can be easily identified by normal cluster ratio. And then the identified cluster can be used in real data detection. In the traditional clustering-based intrusion detection algorithms, clustering using a simple distance-based metric and detection based on the centers of clusters, which generally degrade detection accuracy and efficiency. Our approach based on DSOM clustering can settle these problems effectively. The experiment result shows that our approach can detect unknown intrusions efficiently in the real network connections. © Springer-Verlag Berlin Heidelberg 2005.