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A Novel Classification Via Clustering Method for Anomaly Based Network Intrusion Detection System

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Executive Summary

Intrusion detection in the internet is an active area of research. Intruders can be classified into two types, namely; external intruders who are unauthorized users of the computers they attack, and internal intruders, who have permission to access the system but with some restrictions. The aim of this paper is to present a methodology to recognize attacks during the normal activities in a system. A novel classification via sequential Information Bottleneck (sIB) clustering algorithm has been proposed to build an efficient anomaly based network intrusion detection model.

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