计算机工程与应用2016，Vol.52Issue(7)：122-126,5.DOI:10.3778/j.issn.1002-8331.1405-0141
基于粒子群的加权朴素贝叶斯入侵检测模型
Intrusion detection model of Weighted Navie Bayes based on Particle Swarm Optimization algorithm
任晓奎 1缴文斌 1周丹1
作者信息
- 1. 辽宁工程技术大学 电子与信息工程学院，辽宁 葫芦岛 125105
- 折叠
摘要
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 opti-mized by Particle Swarm Optimization algorithm is proposed. Firstly, the model reduces the dimension of the data sam-ples 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.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Key words
intrusion detection/rough sets theory/Weighted Naive Bayes/Particle Swarm Optimization algorithm分类
计算机与自动化引用本文复制引用
任晓奎,缴文斌,周丹..基于粒子群的加权朴素贝叶斯入侵检测模型[J].计算机工程与应用,2016,52(7):122-126,5.
