Application of Improved PSO-LSSVM on Network Threat Detection
To solve the problem of the design of classifier in net work threat detection, we conduct a simulation experiment for the parameters” optimal on least squares support vector machine (LSSVM) using the classic PSO algorithm, and the experiment shows that uneven distribution of the initial particle swarm exerts a great impact on the results of LSSVM algorithm”s classification.This article proposes an improved PSO-LSSVM algorithm based on Divide-and-Conquer (DCPSO-LSSVM) to split the optimal domain where the parameters of LSSVM are in.It can achieve the purpose of distributing the initial particles uniformly.And using the idea of Divide-and-Conquer, it can split a big problem into multiple sub-problems, thus, completing problems” modularization.Meanwhile, this paper introduces variation factors to make the particles escape from the local optimum.The results of experiment prove that DCPSO-LSSVM has better effect on classification of network threat detection compared with SVM and classic PSO LSSVM.
divide-and-conquer least squares support vector machine (LSSVM) improved PSO classification network threat detection
QI Fumin XIE Xiaoyao JING Fengxuan
Key Laboratory of Information and Computing Science Guizhou Province,Guizhou Normal University,Guiyang 550001,Guizhou,China
国内会议
秦皇岛
英文
418-426
2013-09-01（万方平台首次上网日期，不代表论文的发表时间）