Research of the network intrusion detection method based on support vector machine

Ying Tang; Lixin Xu

doi:10.1117/12.791498

5 March 2008 Research of the network intrusion detection method based on support vector machine

Ying Tang, Lixin Xu

Proceedings Volume 6623, International Symposium on Photoelectronic Detection and Imaging 2007: Image Processing; 662318 (2008) https://doi.org/10.1117/12.791498
Event: International Symposium on Photoelectronic Detection and Imaging: Technology and Applications 2007, 2007, Beijing, China

Abstract

For the growing web intrusion issues, we propose a new method for intrusion detection. In this paper, statistical learning theory (SLT) is introduced to intrusion detection and a method based on support vector machine (SVM) is presented. Theory of SVM is introduced first, and then in data pretreatment, we propose a method of reducing the dimension of primal data sets and a method of transforming eigenvalue from characters to numbers. In virtue of the network data sets which appear variable, small and with high dimension, we introduce the Sequential Minimal Optimization (SMO) algorithm which is especially for large scale problems. The testing result based on the DARPA data show that the method is effective and efficient.

Citation Download Citation

Ying Tang and Lixin Xu "Research of the network intrusion detection method based on support vector machine", Proc. SPIE 6623, International Symposium on Photoelectronic Detection and Imaging 2007: Image Processing, 662318 (5 March 2008); https://doi.org/10.1117/12.791498

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