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基于TSVM的网络入侵检测研究 被引量：5

Study on Network Intrusion Detection Based on TSVM
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摘要 直推式支持向量机(TSVM)是一种直接从已知样本出发对特定的未知样本进行识别和分类的技术。该文提出了基于TSVM的网络入侵检测系统模型,并用实验给出了它在网络入侵检测中的性能表现,分析了它与基于传统归纳式支持向量机(ISVM)的入侵检测系统的性能对比。实验结果表明,将TSVM应用到入侵检测是切实可行的。 Transductive support vector machines （TSVM） classifies the new data vector based on the information only related to this data vector. This paper proposes an anomaly network traffic detection method based on TSVM. The KDD-99 intrusion detection competition data set is used to illustrate the performances of the TSVM, The performance-comparisons of TSVM and traditional inductive SVM are presented. The results show that TSVM can be used in intrusion detection practically.
作者 徐文龙 姚立红 潘理 倪佑生
机构地区 上海交通大学信息安全工程学院
出处 《计算机工程》 EI CAS CSCD 北大核心 2006年第18期138-140,共3页 Computer Engineering
关键词 入侵检测 统计学习 直推式支持向量机 Intrusion detection Statistical learning Transductive support vector machine
分类号 TP393.08 [自动化与计算机技术—计算机应用技术]
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参考文献8

  • 1许建华,张学工.统计学理论基础[M].北京:电子工业出版社,2004.
  • 2陈毅松,汪国平,董士海.基于支持向量机的渐进直推式分类学习算法[J].软件学报,2003,14(3):451-460. 被引量：88
  • 3Burges C J C.A Tutorial on Support Vector Machines for Pattern Recognition[J].Data Mining and Knowledge Discovery,1998,2(2):955-974.
  • 4Thorsten J.Transductive Inference for Text Classification Using Support Vector Machines[C].Proc.of International Conference on Machine Learning.San Francisco,CA,USA:Morgan Kaufmann,1999:200-209.
  • 5Nikola K,Shaoning P.Transductive Support Vector Machines and Applications in Bioinformatics for Promoter Recognition,Letters and Reviews[J].Neural Information Processing,2004,3(2):31-38.
  • 6KDD-99 Dataset[EB/OL].http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.
  • 7How do I Choose Kernel[EB/OL].www.csie.ntu.edu.tw/～cjlin/libsvm/faq.html.
  • 8SVM-Light Support Vector Machine[EB/OL].http://svmlight.joachims.org/.

二级参考文献17

  • 1[1]Vapnik V. The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
  • 2[2]Stitson MO, Weston JAE, Gammerman A, Vovk V, Vapnik V. Theory of support vector machines. Technical Report, CSD-TR-96-17, Computational Intelligence Group, Royal Holloway: University of London, 1996.
  • 3[3]Cortes C, Vapnik V. Support vector networks. Machine Learning, 1995,20:273～297.
  • 4[4]Vapnik V. Statistical Learning Theory. John Wiley and Sons, 1998.
  • 5[5]Gammerman A, Vapnik V, Vowk V. Learning by transduction. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. Wisconsin, 1998. 148～156.
  • 6[6]Joachims T. Transductive inference for text classification using support vector machines. In: Proceedings of the 16th International Conference on Machine Learning (ICML). San Francisco: Morgan Kaufmann Publishers, 1999. 200～209.
  • 7[7]Boser BE, Guyon IM, Vapnik VN. A training algorithm for optimal margin classifiers. In: Haussler D, ed. Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory. Pittsburgh, PA: ACM Press, 1992. 144～152.
  • 8[8]Burges CJC. Simplified support vector decision rules. In: Saitta L, ed. Proceedings of the 13th International Conference on Machine Learning. San Mateo, CA: Morgan Kaufmann Publishers, 1996. 71～77.
  • 9[9]Osuna E, Freund R, Girosi F. An improved training algorithm for support vector machines. In: Proceedings of the IEEE NNSP'97. Amelia Island, FL, 1997. 276～285.
  • 10[10]Joachims T. Making large-scale SVM learning practical. In: Scholkopf, Burges C, Smola A, eds. Advances in Kernel Methods--Support Vector Learning B. MIT Press, 1999.

共引文献88

同被引文献65

引证文献5

二级引证文献44

计算机工程
2006年 第18期

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