A Framework for Robust and Resilient Critical Infrastructure Systems

Jagdish Ch; ra

doi:10.20965/jaciii.2006.p0265

single-jc.php

« previous

JACIII Vol.10 No.3 pp. 265-269

doi: 10.20965/jaciii.2006.p0265

(2006)

Paper:

Views over last 60 days: 394

A Framework for Robust and Resilient Critical Infrastructure Systems

Jagdish Chandra

The Institute for Reliability and Risk Analysis, The George Washington University, Washington, DC 20052, U.S.A

Received:

February 22, 2005

Accepted:

December 21, 2005

Published:

May 20, 2006

Keywords:

complex systems, vulnerability, threat analysis, hybrid dynamical systems, interdependencies

Abstract

Infrastructures such as transportation systems, power grids, communication networks, water resources, health delivery systems, and financial networks/institutions are vital to the safety, security and the well-being of the society. Reliable performance and protection of such systems is of paramount importance. Critical infrastructure systems, when viewed as complex interacting networks, present many interesting technical challenges to the modeling, analysis and simulation community. In this paper, we review the generic structure of such systems from the perspective of robust design and resilient behavior.

Cite this article as:

J. Chandra, “A Framework for Robust and Resilient Critical Infrastructure Systems,” J. Adv. Comput. Intell. Intell. Inform., Vol.10, No.3, pp. 265-269, 2006.

Data files:

References

[1] R. Alo, A. deKorvin, and F. Modave, “Decision making for robust and resilient systems,” Proc. HICSS 36, 2003.
[2] J. Chandra, “Distributed and decentralized information fusion,” Proc. IconIT’2001, pp. 1-11, 2001.
[3] J. Chandra, “Information fusion for critical infrastructure systems,” Proc. SMi’s Fifth Annual Conference on Military Data Fusion, Sept., 2003.
[4] J. Chandra, and G. Ladde, “Stability analysis of stochastic hybrid systems,” Intern. Jour. Of Hybrid Systems, 4, pp. 179-198, 2004.
[5] M. Grabisch, H. Nguyen, and E. Walker, “Fundamentals of Uncertainty Calculi with Applications to Fuzzy Inference,” Kluwer Academic Publications, 1994.
[6] R. Kearfott, and V. Kreinovich, “Application of Interval Computations,” Kluwer Academic Publications, 1996.
[7] F. Modave, and P. Eklund, “A measurement theory perspective for MCDM,” Prcoc. IEEE Fuzzy Systems Conference, pp. 1068-1071, 2001.
[8] R. Rinaldi, “Modeling and simulating critical infrastructures and their interdependencies,” Proc. HICSS-37, 2004.
[9] G. Shafer, “A Mathematical Theory of Evidence,” Princeton University Press, 1976.
[10] N. Singpurwalla, and J. Booker, “ Membership functions and probability measures of fuzzy sets,” The Amer. Statistician, 99, pp. 867-889, 2004.
[11] P. Varshney, “Distributed Detection and Data Fusion,” Springer, 1996.
[12] W. Wright, R. Smith, R. Danek, and P. Greenway, “A generalisable measure of self-organization and emergence,” Artificial Neural Networks-ICANN, Springer, pp. 857-864, 2001.
[13] L. Zadeh, “Fuzzy sets as a basis for a theory of possibility,” Fuzzy Sets and Systems, 1, pp. 3-28, 1978.
[14] L. Zadeh, “Towards a perception-based theory of probabilistic reasoning with imprecise probabilities,” Jour. Stat. Planning and Inference, 105, pp. 233-264, 2002.
[15] “Report: Electric Power Risk assessment,” National Security Telecommunication Advisory Committee, 1997.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] R. Alo, A. deKorvin, and F. Modave, “Decision making for robust and resilient systems,” Proc. HICSS 36, 2003.

[2] [2] J. Chandra, “Distributed and decentralized information fusion,” Proc. IconIT’2001, pp. 1-11, 2001.

[3] [3] J. Chandra, “Information fusion for critical infrastructure systems,” Proc. SMi’s Fifth Annual Conference on Military Data Fusion, Sept., 2003.

[4] [4] J. Chandra, and G. Ladde, “Stability analysis of stochastic hybrid systems,” Intern. Jour. Of Hybrid Systems, 4, pp. 179-198, 2004.

[5] [5] M. Grabisch, H. Nguyen, and E. Walker, “Fundamentals of Uncertainty Calculi with Applications to Fuzzy Inference,” Kluwer Academic Publications, 1994.

[6] [6] R. Kearfott, and V. Kreinovich, “Application of Interval Computations,” Kluwer Academic Publications, 1996.

[7] [7] F. Modave, and P. Eklund, “A measurement theory perspective for MCDM,” Prcoc. IEEE Fuzzy Systems Conference, pp. 1068-1071, 2001.

[8] [8] R. Rinaldi, “Modeling and simulating critical infrastructures and their interdependencies,” Proc. HICSS-37, 2004.

[9] [9] G. Shafer, “A Mathematical Theory of Evidence,” Princeton University Press, 1976.

[10] [10] N. Singpurwalla, and J. Booker, “ Membership functions and probability measures of fuzzy sets,” The Amer. Statistician, 99, pp. 867-889, 2004.

[11] [11] P. Varshney, “Distributed Detection and Data Fusion,” Springer, 1996.

[12] [12] W. Wright, R. Smith, R. Danek, and P. Greenway, “A generalisable measure of self-organization and emergence,” Artificial Neural Networks-ICANN, Springer, pp. 857-864, 2001.

[13] [13] L. Zadeh, “Fuzzy sets as a basis for a theory of possibility,” Fuzzy Sets and Systems, 1, pp. 3-28, 1978.

[14] [14] L. Zadeh, “Towards a perception-based theory of probabilistic reasoning with imprecise probabilities,” Jour. Stat. Planning and Inference, 105, pp. 233-264, 2002.

[15] [15] “Report: Electric Power Risk assessment,” National Security Telecommunication Advisory Committee, 1997.