关键词: 入侵检测, 粗糙集理论, 加权朴素贝叶斯, 粒子群优化算法

Abstract: Traditional Navie Bayes algorithm exists the issues of low inefficiency for the high dimensional and complex intrusion detection. In order to solve this problem, a detection model based on Weighted Naive Bayes which has been optimized by Particle Swarm Optimization algorithm is proposed. Firstly, the model reduces the dimension of the data samples using rough set theory. Secondly, the improved Particle Swarm Optimization algorithm searches the best attribute weights of Weighted Naive Bayes. Finally, Navie Bayes classifier is structured with the best attribute weights to complete detection. Among them, the improved Particle Swarm Optimization algorithm is using the weighting factor to update its position and velocity formula so as to avoid local optimal. The combination of the two algorithms can not only solve the feature redundancy problem of the traditional Navie Bayes algorithm, but also can optimize the strong independence between features. Through the experiments, the model is effective, and the detection rate is improved.

Key words: intrusion detection, rough sets theory, Weighted Naive Bayes, Particle Swarm Optimization algorithm