The Research On Intrusion Detection Of SVM Based On PSO

With the fast development of computer network technology, the trend is to communicate globally using comprehensive open network environment. The network provides the open and shared resources, but there is always security risk. The firewall, once the most popular defensive method, can no longer meet people's demand of network security, the users of network confront with the gradually grave safe problem and network's invasion has become the most terrible threatens of computer and network's safe. As an important and active security mechanism, Intrusion Detection(ID) will reinforce the traditional system security mechanism.Intrusion detection plays more important role in network security today. This paper introduces a method, particle swarm optimization and support vector machine, to intrusion detection system, and presents a new design of ID based on Particle Swarm Optimization(PSO) and Support Vector Machine(SVM). Support vector machine, as a new kind of soft sensor techniques, has been studied widely in the world recently. Support vector machine is based on Vapnik's minimal of the structure risk, tries its best to increase the generalization. When using the method of support vector machine into intrusion detection system, better classification can be acquired at the condition that there is less known knowledge. So the method is applied in the intrusion detection system. The support vector machine parameter decides its study performance and exudes the ability. As the parameter choice is infinite, the parameter choice needs enormous time, and is very difficult to approach superiorly. Since the SVM model depend on a proper setting of its parameters(regulation parameter C and the radial basis function width parameterσ), especially on the interaction of the two parameters, this paper presents an optimal selection approach of the SVM parameters based on particle swarm optimization algorithm. PSO is a new biological evolutionary algorithm, origination from the behavior study of birds'seeking food. It can be implemented with easy principles and a few parameters need to be tuned, as well as it has maximum strength in dealing with high-dimension optimization problems. The experiments show that the optimal parameter selection approach based on PSO is available and the research of intrusion detection based on particle swarm optimization and support vector machine is effective in reducing the number of alerts, false positive, false negative better.The followings are the main contents based on the PSO algorithm, SVM theory and intrusion detection theory.(1) The principle of particle swarm optimization has been presented. According to the different inertia weight has made a comparative experiment on particle swarm optimization.(2) On support vector machine analysis and research, found that Generalization ability of support vector machines depend on the choice of parameters and their mutual relations. To address this issue, the paper put forward to find the optimal parameters(C,σ) of support vector machine.(3) Particle swarm optimization presents an optimal selection approach of the SVM parameters(C,σ).The experimental result shows that the classifier has stronger ability to distinguish garbage messages.