Echidna: efficient clustering of hierarchical data for network traffic analysis

Authors:
Abdun Naser Mahmood;Christopher Leckie;Parampalli Udaya
Affiliations:
Department of Computer Science and Software Engineering, University of Melbourne, Australia;Department of Computer Science and Software Engineering, University of Melbourne, Australia;Department of Computer Science and Software Engineering, University of Melbourne, Australia
Venue:
NETWORKING'06 Proceedings of the 5th international IFIP-TC6 conference on Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile and Wireless Communications Systems
Year:
2006

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Abstract

There is significant interest in the network management community about the need to improve existing techniques for clustering multi-variate network traffic flow records so that we can quickly infer underlying traffic patterns. In this paper we investigate the use of clustering techniques to identify interesting traffic patterns in an efficient manner. We develop a framework to deal with mixed type attributes including numerical, categorical and hierarchical attributes for a one-pass hierarchical clustering algorithm. We demonstrate the improved accuracy and efficiency of our approach in comparison to previous work on clustering network traffic.