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Volume 28 Issue 9
Sep.  2010
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Zhou Ming-zheng. An Anomaly Intrusion Detection Based on Kernel Fisher Discriminant[J]. Journal of Electronics & Information Technology, 2006, 28(9): 1727-1730.
Citation: Zhou Ming-zheng. An Anomaly Intrusion Detection Based on Kernel Fisher Discriminant[J]. Journal of Electronics & Information Technology, 2006, 28(9): 1727-1730.

An Anomaly Intrusion Detection Based on Kernel Fisher Discriminant

  • Received Date: 2005-01-10
  • Rev Recd Date: 2005-06-21
  • Publish Date: 2006-09-19
  • Kernel method is introduced to intrusion detection and an anomaly intrusion detection method based on kernel Fisher discriminant is presented in this paper. This method is applied for monitoring the abnormal behavior of processes. Firstly, this paper presents the possible of kernel Fisher method applied to intrusion detection. Secondly, this paper descriptions the kernel Fisher algorithm is generalized for heterogeneous datasets. A model of anomaly intrusion detection based on kernel Fisher is given and the working process of this model is used with sendmail system call in detail discussion; Finally, the simulation result is compared with other methods, The measuring result of this method is superior to other similar methods .
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  • Anup K Ghosh, Aaron Schwartzbard. A study in using neuralnetworks for anomaly and misuse detection. The 8th USENIX Security Symposium, Washington D C, 1999: 46-57.[2]Balajinath B, Raghavan S V. Intrusion detection through learning behavior model[J].Computer Communications.2001, 24(12):1202-1212[3]Jha S, Tan K, Maxion R A. Markov Chains, classifiers and intrusion detection. The 14th IEEE Computer Security Foundations Workshop, Canada, 2001: 206-215.[4]张箭, 龚俭. 一种基于模糊综合评判的入侵异常检测方法. 计算机研究与发展, 2003, 40(6): 776-782.[5]Fisher R A. The statistical utilization of multiple measurements. Annals of Eugenics, 1938, 6(8): 376-386.[6]Wilson D, Martinez R. Improved heterogeneous distance functions. Journal of Artificial Intelligence Research, 1997, 6(1): 1-34.[7]Lee W, Stolfo SJ. A data mining framework for building intrusion detection medel, In: Gorgl, Keiter M K, eds. Proceedings of He 1999 IEEE Symposium on Security and Privacy, Oakland, CA, IEEE Computer Society Press, 1999: 120-132.[8]Forrest S, Hofmeyr S A, et al.. A sense of self for unix process. In: Proceedings of 1996 IEEE Symposium on Computer Security and Privacy, Canada, 1996: 120-128.[9]Lee W, Stolfo S, Chan P. Learning patterns from unix process execution traces for intrusion detection. In: Proceeding of AAAI Workshop: AI Approaches to Fraud Detection and Risk Management, Washington D C, 1997: 191-197.
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