To solve the problem of the design of classifier in network threat detection, we conduct a simulation experiment for the parameters’ optimal on least squares support vector machine （LSSVM） using the classic PSO alg...To solve the problem of the design of classifier in network 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 PSOLSSVM.展开更多
基金Supported by the Special Fund of Financial Support for Development of Local Universities in China(2012-140 &2012-118)The Science and Technology Foundation of Guizhou Provincial([2011] 2213)Natural Sciences Research Foundation of Guizhou Normal University for Student(201219)
文摘To solve the problem of the design of classifier in network 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 PSOLSSVM.
