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A discussion on “Detection of intrusions in information systems by sequential change-point methods” by Tartakovsky, Rozovskii, Blažek, and Kim. (English) Zbl 1248.94028

Summary: The discussion focuses on issues arising in practical applications of the CUSUM procedures developed by A. G. Tartakovsky et al. [ibid. 3, No. 3, 252–293 (2006; Zbl 1248.94032)] to detect changes in multichannel sensor systems. These issues are illustrated with a data set from the MIT Lincoln Laboratory that is used to estimate the parameters of procedures for detecting denial-of-service attacks.

MSC:

94A13 Detection theory in information and communication theory
68M10 Network design and communication in computer systems
90B18 Communication networks in operations research
62P30 Applications of statistics in engineering and industry; control charts
94A40 Channel models (including quantum) in information and communication theory

Citations:

Zbl 1248.94032
Full Text: DOI

References:

[1] Bagshaw, M.; Johnson, R. A., The effect of serial correlation on the performance of CUSUM tests II, Technometrics, 17, 1, 73-80 (1975)
[2] Johnson, R. A.; Bagshaw, M., The effect of serial correlation on the performance of CUSUM tests, Technometrics, 16, 1, 103-112 (1974) · Zbl 0277.62069
[3] S.-H. Kim, C. Alexopoulos, D. Goldsman, K.-L. Tsui, A new model-free CUSUM chart for autocorrelated processes, Technical Report, School of Industrial and Systems Engineering, Georgia Institute of Technology, 2005. Available via http://www.isye.gatech.edu/ skim/QC_Known_SHK.pdf; S.-H. Kim, C. Alexopoulos, D. Goldsman, K.-L. Tsui, A new model-free CUSUM chart for autocorrelated processes, Technical Report, School of Industrial and Systems Engineering, Georgia Institute of Technology, 2005. Available via http://www.isye.gatech.edu/ skim/QC_Known_SHK.pdf
[4] Montgomery, D. C., Introduction to Statistical Quality Control (2001), Wiley: Wiley New York
[5] Y. Park, A Statistical Process Control Approach for Network Intrusion Detection, Ph.D. Dissertation, School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, 2005; Y. Park, A Statistical Process Control Approach for Network Intrusion Detection, Ph.D. Dissertation, School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, 2005
[6] Runger, G. C.; Willemain, T. R., Model-based and model-free control of autocorrelated processes, J. Qual. Technol., 27, 4, 283-292 (1995)
[7] A.G. Tartakovsky, B.L. Rozovskii, R.B. Blažek, H. Kim, Detection of intrusions in information systems by sequential change-point methods, Stat. Meth. (2006) (in this issue); A.G. Tartakovsky, B.L. Rozovskii, R.B. Blažek, H. Kim, Detection of intrusions in information systems by sequential change-point methods, Stat. Meth. (2006) (in this issue) · Zbl 1248.94032
[8] Wagner, M. A.F.; Wilson, J. R., Using univariate Bézier distributions to model simulation input processes, IIE Trans., 28, 9, 699-711 (1996)
[9] Ye, N.; Li, X.; Chen, Q.; Emran, S. M.; Xu, M., Probabilistic techniques for intrusion detection based on computer audit data, IEEE Trans. Sys. Man Cybern., 31, 4, 266-274 (2001)
[10] Ye, N.; Vilbert, S.; Chen, Q., Computer intrusion detection through EWMA for autocorrelated and uncorrelated data, IEEE Trans. Reliab., 52, 1, 75-82 (2003)
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