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, 01-03 Apr 2009,

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, Professor Dimitrios and Marnerides, Dr Angelos
• 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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