Two-level feature selection method based on SVM for intrusion detection

被引：1

作者：

Wu, Xiao-Nian ^{[1
,2
,3
]}

Peng, Xiao-Jin ^{[1
]}

Yang, Yu-Yang ^{[1
]}

Fang, Kun ^{[1
]}

机构：

[1] School of Communication and Information, Guilin University of Electronic Technology, Guilin

[2] Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory, Guilin University of Electronic Technology, Guilin

[3] Guangxi Experiment Center of Information Science, Guilin University of Electronic Technology, Guilin

来源：

Tongxin Xuebao/Journal on Communications | 2015年 / 36卷 / 04期

关键词：

Feature selection; Fisher score; Intrusion detection; Sequential backward selection; Support vector machine;

D O I：

10.11959/j.issn.1000-436x.2015127

中图分类号：

学科分类号：

摘要：

To select optimized features for intrusion detection, a two-level feature selection method based on support vector machine was proposed. This method set an evaluation index named feature evaluation value for feature selection, which was the ratio of the detection rate and false alarm rate. Firstly, this method filtrated noise and irrelevant features to reduce the feature dimension respectively by Fisher score and information gain in the filtration mode. Then, a crossing feature subset was obtained based on the above two filtered feature sets. And combining support vector machine, the sequential backward selection algorithm in the wrapper mode was used to select the optimal feature subset from the crossing feature subset. The simulation test results show that, the better classification performance is obtained according to the selected optimal feature subset, and the modeling time and testing time of the system are reduced effectively. ©, 2015, Editorial Board of Journal on Communications. All right reserved.

引用

页数：8

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