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基于支持向量机的多层分类入侵检测系统研究

A Research on the Multilayer Classification Intrusion Detection System Based on the Support Vector Machine
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摘要 概述了入侵检测技术和入侵检测系统,研究了支持向量机的线性和非线性分类算法。在此基础上创建了基于支持向量机的多层分类入侵检测系统模型,然后通过计算机仿真实验进行验证测试。 In this paper,the authors outlined Intrusion Detection System and studied the classification al- gorithm of linear and nonlinear of Support Vector Machine. On this basis, a model of the multilayer classification Intrusion Detection System based on the Support Vector Machine was established and then was verified through the computer simulation tests.
作者 朱红斌 蔡郁
机构地区 丽水学院计算机与信息工程学院 丽水市国家税务局信息中心
出处 《丽水学院学报》 2008年第2期54-57,共4页 Journal of Lishui University
关键词 入侵检测 支持向量机 多层分类 仿真测试 intrusion detection support vector machine multi--classification simulation test
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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  • 1[1]Forrest S, Perrelason AS, Allen L, Cherukur R. Self_Nonself discrimination in a computer. In: Rushby J, Meadows C, eds. Proceedings of the 1994 IEEE Symposium on Research in Security and Privacy. Oakland, CA: IEEE Computer Society Press, 1994. 202～212.
  • 2[2]Ghosh AK, Michael C, Schatz M. A real-time intrusion detection system based on learning program behavior. In: Debar H, Wu SF, eds. Recent Advances in Intrusion Detection (RAID 2000). Toulouse: Spinger-Verlag, 2000. 93～109.
  • 3[3]Lee W, Stolfo SJ. A data mining framework for building intrusion detection model. In: Gong L, Reiter MK, eds. Proceedings of the 1999 IEEE Symposium on Security and Privacy. Oakland, CA: IEEE Computer Society Press, 1999. 120～132.
  • 4[4]Vapnik VN. The Nature of Statistical Learning Theory. New York: Spring-Verlag, 1995.
  • 5[5]Lee W, Dong X. Information-Theoretic measures for anomaly detection. In: Needham R, Abadi M, eds. Proceedings of the 2001 IEEE Symposium on Security and Privacy. Oakland, CA: IEEE Computer Society Press, 2001. 130～143.
  • 6[6]Warrender C, Forresr S, Pearlmutter B. Detecting intrusions using system calls: Alternative data models. In: Gong L, Reiter MK, eds. Proceedings of the 1999 IEEE Symposium on Security and Privacy. Oakland, CA: IEEE Computer Society Press, 1999. 133～145.
  • 7E Eskin.Anomaly detection over noisy data using learned probability distributions[A].Proceedings of the 17th International Conference on Machine Learning[C].San Mateo,CA:Morgan Kaufmann,2000.255-262.
  • 8T Lane,C Brodley.Temporal sequence learning and data reduction for anomaly detection[J].ACM Trans Info System Security,1999,2:295-331.
  • 9T Lane,C E Brodley.Data reduction techniques for instancebased learning from human/computer interface data[A].Proceedings of the 17th International Conference on Machine Learning[C].San Mateo,CA:Morgan Kaufmann,2000.519-526.
  • 10D Dasgupta,F Gonzalez.An immunity-based technique to characterize intrusions in computer networks[J].IEEE Transactions on Evolutionary Computation,2002,3(6):281-291.

共引文献246

丽水学院学报
2008年 第2期

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