A Feature Selection Algorithm to Intrusion Detection Based on Cloud model and Multi- Objective Particle Swarm Optimization
there exist many problems in intrusion detection system such as large number of data volume and features, data redundancy and so on, which seriously affected the efficiency of the assessment. In this paper, we propose an approach called EFSA-CP to intrusion detection based on Cloud model and improved multi-objective Particle Swarm Optimization. The algorithm evaluates the characteristics of the attribute weights by the Cloud model and generates the optimal feature subsets which achieve the best trade-off between detection rate and rate of false alarm by MOPSO, which solves the problem of feature redundancy and helps improve the speed of the evaluation. Experimental results show that EFSA-CP can solve the feature selection problem of intrusion detection effectively. It can also achieve balanced detection performance on different types of attacks, with better convergence at the same time.
component Cloud model feature selection intrusion detection multi-objective particle swarm optimization
Liu-Hong Zhou Yan-Hua Liu Guo-Long Chen
College of Mathematics and Computer Science Fuzhou University Fuzhou, China
国际会议
杭州
英文
562-565
2011-10-28（万方平台首次上网日期，不代表论文的发表时间）