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.
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Mahmood, A.N., Leckie, C., Udaya, P. (2006). Echidna: Efficient Clustering of Hierarchical Data for Network Traffic Analysis. In: Boavida, F., Plagemann, T., Stiller, B., Westphal, C., Monteiro, E. (eds) NETWORKING 2006. Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile and Wireless Communications Systems. NETWORKING 2006. Lecture Notes in Computer Science, vol 3976. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11753810_92
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DOI: https://doi.org/10.1007/11753810_92
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34192-5
Online ISBN: 978-3-540-34193-2
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