The Application of the Relative Entropy Density Divergence in Intrusion Detection Models
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[1] Zhong Anming,et al. Study on the applications of hidden Markov models to computer intrusion detection , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).
[2] Wei Wang,et al. Modeling program behaviors by hidden Markov models for intrusion detection , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).
[3] Kymie M. C. Tan,et al. Determining the operational limits of an anomaly-based intrusion detector , 2003, IEEE J. Sel. Areas Commun..
[4] Dong Xiang,et al. Information-theoretic measures for anomaly detection , 2001, Proceedings 2001 IEEE Symposium on Security and Privacy. S&P 2001.
[5] M. Do. Fast approximation of Kullback-Leibler distance for dependence trees and hidden Markov models , 2003, IEEE Signal Processing Letters.
[6] Yonggang Pang,et al. A hidden Markov models-based anomaly intrusion detection method , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).
[7] R. Gray. Entropy and Information Theory , 1990, Springer New York.
[8] Guofei Gu,et al. Measuring intrusion detection capability: an information-theoretic approach , 2006, ASIACCS '06.
[9] Boris Skoric,et al. Towards an Information-Theoretic Framework for Analyzing Intrusion Detection Systems , 2006, ESORICS.
[10] Fady Alajaji,et al. The Kullback-Leibler divergence rate between Markov sources , 2004, IEEE Transactions on Information Theory.