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.