Network intrusion detection based on multi-class support vector machine

Authors:
Anh Vu Le;Hoai An Le Thi;Manh Cuong Nguyen;Ahmed Zidna
Affiliations:
Laboratory of Theoretical and Applied Computer Science, UFR MIM, University of Lorraine, Ile du Saulcy, Metz, France;Laboratory of Theoretical and Applied Computer Science, UFR MIM, University of Lorraine, Ile du Saulcy, Metz, France;Laboratory of Theoretical and Applied Computer Science, UFR MIM, University of Lorraine, Ile du Saulcy, Metz, France;Laboratory of Theoretical and Applied Computer Science, UFR MIM, University of Lorraine, Ile du Saulcy, Metz, France
Venue:
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
Year:
2012

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Abstract

In the field of network security, the Intrusion Detection Systems (IDSs) always require more research to improve system performance. Multi-Class Support Vector Machine (MSVM) has widely used for network intrusion detection to perform the multi-class classification of intrusions. In this paper, we first consider the MSVM model introduced by J. Weston and C. Watkins that differs from classical approaches for MSVM. Further, as an alternative approach, we use a pseudo l∞-norm proposed by Y. Guermeur instead of l2-norm in the previous model. Both models are investigated to IDSs and tested on the KDD Cup 1999 dataset, a benchmark data in the researches on network intrusion detection. Computational results show the efficiency of both models to IDSs, in particular the alternative model with the l∞-norm.