Unsupervised two-class and multi-class support vector machines for abnormal traffic characterization

Marnerides, A., Pezaros, D., Kim, H. and Hutchison, D. (2009) Unsupervised two-class and multi-class support vector machines for abnormal traffic characterization. In: 10th International Passive and Active Measurements Conference (PAM 2009), Seoul, Korea,

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Abstract

Although measurement-based real-time traffic classification has received considerable research attention, the timing constraints imposed by the high accuracy requirements and the learning phase of the algorithms employed still remain a challenge. In this paper we propose a measurement-based classification framework that exploits unsupervised learning to accurately categorise network anomalies to specific classes. We introduce the combinatorial use of two-class and multi-class unsupervised Support Vector Machines (SVM)s to first distinguish normal from anomalous traffic and to further classify the latter category to individual groups depending on the nature of the anomaly.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Pezaros, Dr Dimitrios
Authors: Marnerides, A., Pezaros, D., Kim, H., and Hutchison, D.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
First Published:First published in Proceedings of the 10th International Passive and Active Measurements Conference (PAM 2009)
Publisher Policy:Reproduced with permission of the author
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