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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Vapnik, V.N.: An overview of statistical learning theory. J. IEEE transactions on Neural Networks 10, 988–1000 (1999)
Huang, H.P., Yang, F.C., et al.: Intrusion Detection Based on Active Networks. J. Information Science and Engineering 25, 843–859 (2005)
Yu, J., Lee, H., et al.: Traffic flooding attack detection with SNMP MIB using SVM. J. Computer Communications 31, 4212–4219 (2008)
Song, J., Takakura, H., et al.: Unsupervised Anomaly Detection Based on Clustering and Multiple One-Class SVM. J. IEICE Transactions on Communications E92-B, 1981–1990 (2009)
Kim, D.S., Nguyen, H.-N., Park, J.S.: Genetic algorithm to improve SVM basednetwork intrusion detection system. In: 19th International Conference on Advanced Information Networking and Applications, pp. 155–158. IEEE Press, Taiwan (2005)
Cortes, C., Vapnik, V.: Support Vector Networks. J. Machine learning 20, 273–297 (1995)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. J. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Qiong, Z., Yingsha, Z.: Hierarchical clustering of gene expression profiles with graphics hardware acceleration. J. Pattern Recognition Letters 27, 676–681 (2006)
Anderson, J.A., Lorenz, C.D., Travesset, A.: General Purpose Molecular Dynamics Simulations Fully Implemented on Graphics Processing Units. J. Computational Physics 227, 5342–5359 (2008)
Garland, M., Le Grand, S., Nickolls, J., Anderson, J., Hardwick, J., Morton, S., Phillips, E., Yao, Z., Volkov, V.: Parallel Computing Experiences with CUDA. J. IEEE Micro. 28, 13–27 (2008)
CUDA_C_Programming Guide, http://developer.nvidia.com/object/cuda_3_2_downloads.html
Batcher, K.: Sorting networks and their applications. In: Proc. AFIPS Spring Joint Computer Conference, pp. 307–314. ACM Press, New York (1968)
Bitonic Sorting, http://facultyfp.salisbury.edu/taanastasio/COSC490/Fall03/Lectures/Sorting/bitonic.pdf
KDDCUP 1999 dataset, http://kdd.ics.uci.edu/dataset/kddcup99/kddcup99.htm
LIBSVM - A Library for Support Vector Machines, http://www.csie.ntu.edu.tw/~cjlin/libsvm/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-642-24999-0_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24998-3
Online ISBN: 978-3-642-24999-0
eBook Packages: Computer ScienceComputer Science (R0)