%0 Journal Article
%T Application of an Improved SVM Multiª²Class Classification to Intrusion Detection
%A LI Tai-bai
%A TANG Wan-mei
%J Journal of Chongqing Normal University
%D 2012
%I Chongqing Normal University
%X Intrusion detection system as the key technology of network security becomes research hot spot of the current network security, while precision and generalization performance is the key point of intrusion detection algorithm. According to binary tree method and the characteristics of sphere structured support vector machine, an improved SVM multiª²class classification algorithm is proposed to intrusion detection. This algorithm uses similarity functions as weight value and selects two kinds of sample similarity minimum to structure twoª²class classifier; to bottomª²up structure kinds of twoª²class classifier of sphere structured SVM. Finally it is applied to intrusion detection. The KDD CUP 1999 intrusion detection data used to simulate experiments. Experimental results show that the algorithm effectively improved the detection accuracy and generalization performance.
%K Support Vector Machine
%K sphere structure
%K binary tree
%K intrusion detection
%U http://journal.cqnu.edu.cn/1205/pdf/120515.pdf