电子科技大学学报2018，Vol.47Issue(2)：267-271,5.DOI:10.3969/j.issn.1001-0548.2018.02.017
一种基于信息论模型的入侵检测特征提取方法
An Intrusion Detection Feature Extraction Method Based on Information Theory Model
摘要
Abstract
In the network intrusion detection, because of the high dimensionality and redundant features of the original data, the storage burden of the intrusion detection system is increased, and the performance of the classifier is reduced. Aiming at this problem, this paper proposes an intrusion detection feature extraction method based on information theory model. The method starts with the feature of maximum information gain, and then iteratively adjusts the correlation among the classification mark of the data set, selected feature subset and candidate feature by search strategies and evaluation functions. Finally, the feature subset is determined by terminating conditions. In the experiment, we chose sample dataset for intrusion detection as the experimental data, and apply feature vector selected by the method to the support vector machine classification algorithm. It is found that the detection accuracy is almost unchanged, in the case that the dimension of the feature is greatly reduced. The results show the validity of the method.关键词
特征选择/信息熵/入侵检测/互信息/半监督Key words
feature selection/information entropy/intrusion detection/mutual information/semi-supervised分类
信息技术与安全科学引用本文复制引用
宋勇,蔡志平..一种基于信息论模型的入侵检测特征提取方法[J].电子科技大学学报,2018,47(2):267-271,5.基金项目
国家自然科学基金(601379145) （601379145）
