Research of the network intrusion detection method based on support vector machine
Ying Tang, Lixin Xu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
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.