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

Marnerides, Angelos K. 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, Seoul, South 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 or Workshop Item (Other)
    Journal or Publication Title: Passive and Active Measurements (PAM) Conference Student Workshop 2009
    Uncontrolled Keywords: networkresilience ; anaproject
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Q Science > QA Mathematics
    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: 27 Jul 2012 02:11
    Identification Number:
    URI: http://eprints.lancs.ac.uk/id/eprint/34653

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