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Approach to Anomaly Traffic Detection in a Local Network

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【作者】 王秀英肖立中邵志清

【Author】 WANG Xiu-ying 1,2,XIAO Li-zhong 2,3,SHAO Zhi-qing2 1 Department of Computer Information,Shanghai Xinqiao Vocational and Technical College,Shanghai 200237,China2 School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,China3 Department of Computer Science and Information Engineeting,Shanghai Institute of Technology,Shanghai 200235,China

【机构】 Department of Computer Information,Shanghai Xinqiao Vocational and Technical CollegeSchool of Information Science and Engineering,East China University of Science and TechnologyDepartment of Computer Science and Information Engineeting,Shanghai Institute of Technology

【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.

【Abstract】 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.

【基金】 Shanghai Education Commission Foundation for Excellent Young High Education Teachers,China(No.xqz05001;No.YYY-07008)
  • 【文献出处】 Journal of Donghua University(English Edition) ,东华大学学报(英文版) , 编辑部邮箱 ,2009年06期
  • 【分类号】TP393.08
  • 【被引频次】1
  • 【下载频次】30
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