Feature Selection Method for Network Intrusion Based on Fast Attribute Reduction of Rough Set
Aiming at the problem that independent and redundancy attributes cause classification algorithms low detection speed and detection rate in network intrusion detection. Hence,a novel feature selection approach for network intrusion based on fast attribute reduction of rough set is proposed in the paper. First,the approach removes independent attributes according to normalized mutual information between condition attributes and decision attributes,then an improved formula for measuring attribute importance based on positive region of rough set is presented. Finally,a fast and recursive attribute reduction method is designed to realize feature selection of network intrusion. KDDCUP1999 data-set are used to experiment. The experimental result shows that compared with similar algorithms,the approach is more effective and efficient in discarding independent and redundancy attributes and in improving intrusion detection performance of classification algorithm.
rough set normalized mutual information attribute Reduction intrusion Detection
Guohua Geng Na Li Shangfu Gong
School of Information Science and Technology,Northwest University,Xian,China School of Computer,Xian University of Science and Technology,Xian,China
国际会议
西安
英文
2136-2140
2011-12-23（万方平台首次上网日期，不代表论文的发表时间）