A Research on Intrusion Detection Based on Support Vector Machines
Mass of the training samples and setting parameters of SVM artificially will affect badly the efficiency to find an optimal decision hyperplane for SVM.In this paper, FCM clustering algorithm and heuristic PSO algorithm are applied to Intrusion Detection. FCM clustering algorithm is designed to help SVM to find the optimal training samples from vast amounts of data;heuristic PSO algorithm is designed to find optimal parameters for SVM intelligently. The result of simulations run on the data of KDDCUP1999 shows that this approach can not only reduce the number of training samples and training time for SVM, but also detect unknown and known intrusions efficiently in the network.
Support Vector Machines Fuzzy C-means Clustering Support vector Particle Swarm Optimization
Xiaozhao Fang Wei Zhang Shaohua Teng Na Han
Faculty of Computer, Guangdong University of technology Guangzhou,Guangdong, P. R. China
国际会议
南宁
英文
109-112
2010-10-13（万方平台首次上网日期，不代表论文的发表时间）