文献类型：期刊文献

中文题名：Approach to Anomaly Traffic Detection in a Local Network

英文题名：Approach to anomaly traffic detection in a local network

作者：Wang, Xiu-Ying[1,2]; Xiao, Li-Zhong[2,3]; Shao, Zhi-Qing[2]

机构：[1] Department of Computer Information, Shanghai Xinqiao Vocational and Technical College, Shanghai 200237, China; [2] School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China; [3] Department of Computer Science and Information Engineeting, Shanghai Institute of Technology, Shanghai 200235, China

年份：2009

卷号：26

期号：6

起止页码：656

中文期刊名：Journal of Donghua University(English Edition)

外文期刊名：Journal of Donghua University (English Edition)

收录：EI(收录号：20101512844597);Scopus

基金：Shanghai Education Commission Foundation for Excellent Young High Education Teachers,China(No.xqz05001;No.YYY-07008)

语种：英文

中文关键词：clanger theory; information enlropy; ID3 algorithm ; abnormal traffic

外文关键词：Behavioral research

摘要：The research intends to solve the problem of the occupation of bandwidth of local network by abnormal traffic which affects normal user's network behaviors.Firstly,a new algorithm in this paper named danger-theory-based abnormal traffic detection was presented.Then an advanced ID3 algorithm was presented to classify the abnormal traffic.Finally a new model of anomaly traffic detection was built upon the two algorithms above and the detection results were integrated with firewall.The firewall limits the bandwidth based on different types of abnormal traffic.Experiments show the outstanding performance of the proposed approach in real-time property,high detection rate,and unsupervised learning.
The research intends to solve the problem of the occupation of bandwidth of local network by abnormal traffic which affects normal user's network behaviors. Firstly, a new algorithm in this paper named danger-theory-based abnormal traffic detection was presented. Then an advanced ID3 algorithm was presented to classify the abnormal traffic. Finally a new model of anomaly traffic detection was built upon the two algorithms above and the detection results were integrated with firewall. The firewall limits the bandwidth based on different types of abnormal traffic. Experiments show the outstanding performance of the proposed approach in real-time property, high detection rate, and unsupervised learning.