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

Marnerides, Angelos and Pezaros, Dimitrios P. and Kim, Hyun-chul and Hutchison, David (2009) Unsupervised two-class & multi-class support vector machines for abnormal traffic characterization. In: Passive and Active Measurements (PAM) Conference Student Workshop 2009, 2009-01-012009-01-04.

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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: Contribution to Conference (Other)
Journal or Publication Title: Passive and Active Measurements (PAM) Conference Student Workshop 2009
Uncontrolled Keywords: /dk/atira/pure/researchoutput/libraryofcongress/qa
Subjects:
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 34653
Deposited By: Mr Angelos K. Marnerides
Deposited On: 30 Nov 2010 08:51
Refereed?: Yes
Published?: Published
Last Modified: 21 Sep 2019 23:16
URI: https://eprints.lancs.ac.uk/id/eprint/34653

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