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基于融合免疫算法和RBF网络的入侵检测系统 被引量:6

Intrusion Detection System Based on Combination of Immune Arithmetic and RBF Network
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摘要 入侵检测技术是网络信息安全技术中很重要的一个研究领域。为了提高入侵检测系统对入侵类型的识别能力,在该系统中将免疫算法与RBF网络融合起来,形成一种双层分类结构。试验结果表明,基于融合免疫算法和RBF网络的入侵检测系统能有效地区分4种入侵类型。 Intrusion detection technology is a very important research field on network and information security technology. In order to improve the distinguish capability of intrusion detection system, immune arithmetic and RBF network are combinated in the intrusion detection system, which is a double-layer classifiable structure. Experimental results show that the intrusion detection system based on the combination of immune arithmetic and RBF network can efficiently distinguish four attack types.
出处 《计算机工程》 CAS CSCD 北大核心 2007年第9期160-162,共3页 Computer Engineering
基金 湖南省教育厅自然科学基金资助项目(05C245) 湖南省自然科学基金资助项目(00JJY2059)
关键词 入侵检测 免疫算法 RBF网络 双层分类 Intrusion detection Immune arithmetic RBF network Double-layer classification
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  • 1Tarun Ambwani.Multi Class Support Vector Machine Implementation to Intrusion detection[C].In:2003 International Joint Conference on Neural Network,2003:2300~2305
  • 2Yu Guan,Ali A Ghorbani,Nabil Belacel.Y-means:a clustering method for intrusion detection[C].In:2003 Canadian Conference on Electrical and Computer Engineering,2003:1083~1086
  • 3S Mukkamala,G Janoski,Andrew Sung Intrusion Detection Using Neural Networks and Support Vector Machines[C].In:2002 International Joint Conference on Neural Network,2002:1702~1707
  • 4M Cococcioni,G Frosini,B Lazzerini et al.A New Approach to Combining Outputs of Multiple Classifiers[C].In:Annual Meeting of the North American Fuzzy Information Processing Society,2002:400~405
  • 5Chee Peng Lim,Phaik Yean Goay,Poh Suan Teoh et al.combination of decision from a multiple neyral network classifier system[C].In:1999 third international conference on knowledge-based intelligent information engineering systems,1999:191~194
  • 6http://kdd.ics.uci.edu/databases/kddcup99/task.htral
  • 7Jou Yfrank,Rome Lab USA Tech Rep:CDRL A005 1997
  • 8Huang Deshuang,Int J Pattern Recognition Artifical Intelligence,1997年,11卷,6期,873页
  • 9Lee S W,Neural Networks,1995年,8卷,5期,783页
  • 10李腊元.通信协议形式化模型的研究[J].计算机学报,1998,21(5):419-427. 被引量:4

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