Research of support vector machine classifiers for intrusion detection

LEI Xiangyu, ZHOU Ping

1. School of Computer Science and Engineering, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China
• Online:2013-06-01 Published:2013-06-14

支持向量分类机在入侵检测中的应用研究

1. 桂林电子科技大学 计算机科学与工程学院，广西 桂林 541004

Abstract: To enhance the approximation and generalization ability of intrusion detection system, theoretical framework of multiple classifiers is analyzed, and factors such as training data pretreatment, cross-validation time and intrusion detection model accuracy is also taken into consideration. In order to get optimal parameters rapidly, a new approach based on grid search is presented. The KDD dataset is mapped into a high-dimensional feature space via the method for intrusion detection based on support vector machine. Different algorithms are applied to optimize the related parameters for kernel function. By using improved grid search method, the acquired parameter has relatively obvious time superiority. The experimental results prove that the classification accuracy and efficiency are improved.