PSVM: Quantitative Analysis Method of Intelligent System Risk in Independent Host Environment

Abstract

Quantitative risk analysis of security incidents is a typical non-linear classification problem under limited samples. Having advantages of strong generalization ability and fast learning speed, the Support Vector Machine (SVM) is able to solve classification problems in limited samples. To solve the problem of multi-classification, Decision Tree Support Vector Machine (DT-SVM) algorithm is used to construct multi-classifier to reduce the number of classifiers and eliminate non-partitionable regions. Particle Swarm Optimization (PSO) algorithm is introduced to cluster training samples to improve the classification accuracy of the constructed multi-classifier. In the ubiquitous network, the cost of information extraction and processing is significantly lower than that of traditional networks. This paper presents a quantitative analysis method of security risk based on Particle Swarm Optimization Support Vector Machine (PSO-SVM), and classifies the flow data by combining the way of obtaining the flow data in ubiquitous networks, so as to realize the quantitative analysis of the security risk in ubiquitous networks.

In the experiment, KDD99 data set is selected to verify the effectiveness of the algorithm. The experimental results show that the proposed PSO-SVM classification method is more accurate than the traditional one. In the ubiquitous network, this paper builds an experimental environment to illustrate the implementation process of security risk analysis method based on PSO-SVM. The risk analysis results show that the analysis value of risk in ubiquitous network fits well with the change trend of actual value. It means quantitative analysis of risk can be achieved.