A wavelet transform based support vector machine ensemble algorithm and its application in network intrusion detection

被引：0
|
作者：
Nan, Lin [1 ]
Xiang Chun-zhi [2 ]
机构：
[1] Zheng Zhou Univ, Coll Software Technol, Zhengzhou 450002, Henan, Peoples R China
[2] Henan Radio & TV Univ, Educ Technol Ctr, Henan Zhengzhou 450008, Peoples R China
来源：
2014 FIFTH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND ENGINEERING APPLICATIONS (ISDEA) | 2014年
关键词：
Intrusion detection; redundant attributes; wavelet transform; support vector machine ensemble;
D O I：
10.1109/ISDEA.2014.32
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Traditional network intrusion detection algorithms are time consuming due to the existence of redundant attributes. In order to improve the efficiency of network intrusion detection, in this paper, we propose a wavelet transform based support vector machine ensemble algorithm. Firstly, we use wavelet transform to remove the redundant attributes from the original dataset. Then we train a support vector machine ensemble on the simplified dataset. As the wavelet transform in this algorithm can effectively remove the redundant attributes, the proposed algorithm is with high efficiency. Simulation experiments on KDD CUP 99 data set show that the proposed algorithm has good intrusion detection performance.
引用
收藏
页码：109 / 113
页数：5
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