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基于互信息可信度的贝叶斯网络入侵检测研究
引用本文:令狐红英,陈梅,王翰虎,娄皴. 基于互信息可信度的贝叶斯网络入侵检测研究[J]. 计算机工程与设计, 2009, 30(14)
作者姓名:令狐红英  陈梅  王翰虎  娄皴
作者单位:贵州大学计算机科学与信息学院,贵州,贵阳,550025
基金项目:贵州省省级信息化专项基金,贵州省科技计划工业攻关基金 
摘    要:传统贝叶斯入侵检测算法没有考虑不同属性和属性权值对入侵检测结果的影响,因此分类准确率不够高.针对传统贝叶斯入侵检测算法存在的不足,提出基于互信息可信度的贝叶斯网络入侵检测算法.在综合考虑网络入侵检测数据特点和传统贝叶斯分类算法优点的基础上,用互信息相对可信度进行特征选择,删除一些冗余属性,把互信息相对可信度作为权值引进贝叶斯分类算法中,得到优化的贝叶斯网络入侵检测算法(MI-NB).实验结果表明,MI-NB算法能大大降低分类数据的维数,比传统贝叶斯入侵检测算法及改进算法有更高的分类准确率.

关 键 词:特征选择  互信息  可信度  贝叶斯分类  入侵检测

Bayesian network intrusion detection method based on credibility of mutual information
LINGHU Hong-ying,CHEN Mei,WANG Han-hu,LOU Yi. Bayesian network intrusion detection method based on credibility of mutual information[J]. Computer Engineering and Design, 2009, 30(14)
Authors:LINGHU Hong-ying  CHEN Mei  WANG Han-hu  LOU Yi
Abstract:Traditional Bayesian intrusion detection algorithm does not consider the influence caused by different properties and weights of the properties, so the classification accuracy rate is not high enough. Aiming at the shortage of traditional Bayesian intrusion detection algorithm, a Bayesian network intrusion detection method based on credibility of mutual information is proposed. After considering the characteristics of network intrusion detection data and the merits of traditional Bayesian classification, credibility of mutual information is used to select feature, and some redundant properties are deleted. The credibility as weights is introduced Bayesian classifier in order to get optimized Bayesian network intrusion detection algorithm (MI-NB). Experiments show that MI-NB algorithm can greatly reduce the dimension of classification data and has higher classification accuracy rate than the traditional intrusion detection algorithm and the improved algorithm.
Keywords:feature selection  mutual information  credibility  Bayesian classifier  intrusion detection
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