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

Research output: Contribution to conference - Without ISBN/ISSN Otherpeer-review

Publication date1/04/2009
<mark>Original language</mark>English
EventPassive and Active Measurements (PAM) Conference Student Workshop 2009 - Seoul, South Korea
Duration: 1/01/20094/01/2009


ConferencePassive and Active Measurements (PAM) Conference Student Workshop 2009
CitySeoul, South Korea


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.