Abstract
Recently support vector machines based intrusion detection methods are increasingly being researched because it can detect unknown attacks. But solving a support vector machine problem is a typical quadratic optimization problem, which is influenced by the number of training samples. Due to GPU’s high performance in parallel computing, this paper proposes a Euclidean distance based reduction algorithm developed on GPU platform, which is called GPU-RSVM, to eliminate samples that have less effect on building SVM classifier. Experiment results show that the time of reduction process can decrease significantly. With optimal reduction ratio, the overall performance of the intrusion detection classifier based on the proposed GPU-RSVM algorithm is better than that based on LIBSVM algorithm.
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Zhang, X., Zhao, C., Wu, J., Song, C. (2011). A GPU-RSVM Based Intrusion Detection Classifier. In: Zhou, Q. (eds) Theoretical and Mathematical Foundations of Computer Science. ICTMF 2011. Communications in Computer and Information Science, vol 164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24999-0_13
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DOI: https://doi.org/10.1007/978-3-642-24999-0_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24998-3
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