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Unsupervised Learning Techniques for an Intrusion Detection System

Overview With the continuous evolution of the types of attacks against computer networks, traditional intrusion detection systems, based on pattern matching and static signatures, are increasingly limited by their need of an up-to-date and comprehensive knowledge base. Data mining techniques have been successfully applied in host-based intrusion detection. Applying data mining techniques on raw network data, how-ever, is made difficult by the sheer size of the input; this is usually avoided by discarding the network packet contents. This paper introduces a two-tier architecture to over-come this problem: the first tier is an unsupervised clustering algorithm which reduces the network packets payload to a tractable size.

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Publisher: Association for Computing Machinery
File Format: PDF
Date Published: May 21, 2008
Format: White Papers
Topics: Intrusion Detection Systems, Network Security, Security Tools

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