关键词: 入侵检测, 邻域粗糙集, 支持向量机, 粒子群算法

Abstract: The intrusion detection data contains large redundant and noisy features, as well as some continuous attributes. This paper presents an algorithm based on neighborhood rough set and Particle Swarm Optimization algorithm for the effect of intrusion detection. Training subset is reduced by using neighborhood rough set, and new training subset is generated. The redundant attributes and noise are eliminated to avoid information loss when using traditional rough set; parameters of Support Vector Machine are optimized using particle swarm algorithm to avoid the risk of low precision by subjective choiced parameters. It improves the performance of the intrusion detection. The simulation results in the KDD99 dataset show that the algorithm can effectively improve the accuracy and efficiency of intrusion detection. It has high generalization and stability.

Key words: network intrusion detection, neighborhood rough set, Support Vector Machine（SVM）, Particle Swarm Optimization（PSO） algorithm