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A Methodology for Monitoring and Control Network Design

  • István KissEmail author
  • Béla Genge
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10242)

Abstract

The accelerated advancement of Industrial Control Systems (ICS) transformed the traditional and completely isolated systems view into a networked and inter-connected “system of systems” perspective. This has brought significant economical and operational benefits, but it also provided new opportunities for malicious actors targeting critical ICS. In this work we adopt a Cyber Attack Impact Assessment (CAIA) technique to develop a systematic methodology for evaluating the risk levels of ICS assets. The outcome of the risk assessment is integrated into an optimal control network design methodology. Experiments comprising the Tennessee Eastman chemical plant, the IEEE 14-bus electricity grid and the IEEE 300-bus New England electricity grid show the applicability and effectiveness of the developed methodology.

Keywords

Industrial Control Systems Impact assessment Cyber attack Optimal network design Risk assessment 

Notes

Acknowledgment

This work was supported by a Marie Curie FP7 Integration Grant within the 7th European Union Framework Programme (Grant no. PCIG14-GA-2013-631128).

References

  1. 1.
    AIMMS: Advanced Interactive Multidimensional Modeling System (2015). http://www.aimms.com/aimms/. Accessed May 2016
  2. 2.
    Bilis, E., Kroger, W., Nan, C.: Performance of electric power systems under physical malicious attacks. IEEE Syst. J. 7(4), 854–865 (2013)CrossRefGoogle Scholar
  3. 3.
    Carro-Calvo, L., Salcedo-Sanz, S., Portilla-Figueras, J.A., Ortiz-Garca, E.: A genetic algorithm with switch-device encoding for optimal partition of switched industrial Ethernet networks. J. Netw. Comput. Appl. 33(4), 375–382 (2010)CrossRefGoogle Scholar
  4. 4.
    Chen, T., Abu-Nimeh, S.: Lessons from Stuxnet. Computer 44(4), 91–93 (2011)CrossRefGoogle Scholar
  5. 5.
    CrySiS Lab: sKyWIper (a.k.a. flame a.k.a. flamer): a complex malware for targeted attacks, May 2012Google Scholar
  6. 6.
    Downs, J.J., Vogel, E.F.: A plant-wide industrial process control problem. Comput. Chem. Eng. 17(3), 245–255 (1993)CrossRefGoogle Scholar
  7. 7.
    Ford, D.N.: A behavioral approach to feedback loop dominance analysis. Syst. Dyn. Rev. 15(1), 3–36 (1999)CrossRefGoogle Scholar
  8. 8.
    Genge, B., Haller, P., Kiss, I.: Cyber-security-aware network design of industrial control systems. IEEE Syst. J. 11(3), 1373–1384 (2015)CrossRefGoogle Scholar
  9. 9.
    Genge, B., Siaterlis, C.: Physical process resilience-aware network design for SCADA systems. Comput. Electr. Eng. 40(1), 142–157 (2014)CrossRefGoogle Scholar
  10. 10.
    Genge, B., Kiss, I., Haller, P.: A system dynamics approach for assessing the impact of cyber attacks on critical infrastructures. IJCIP 10, 3–17 (2015)Google Scholar
  11. 11.
    Hines, P., Blumsack, S., Cotilla Sanchez, E., Barrows, C.: The topological and electrical structure of power grids. In: 2010 43rd Hawaii International Conference on System Sciences (HICSS), pp. 1–10, January 2010Google Scholar
  12. 12.
    Kiss, I., Genge, B., Haller, P.: Behavior-based critical cyber asset identification in Process Control Systems under Cyber Attacks. In: 16th Carpathian Control Conference (ICCC), pp. 196–201, May 2015Google Scholar
  13. 13.
    Kundur, D., Feng, X., Liu, S., Zourntos, T., Butler-Purry, K.: Towards a framework for cyber attack impact analysis of the electric smart grid. In: First SmartGridComm, pp. 244–249, October 2010Google Scholar
  14. 14.
    Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., Hoffman, A., Giovannini, E.: Handbook on Constructing Composite Indicators. OECD Publishing, Paris (2005)CrossRefGoogle Scholar
  15. 15.
    Sandberg, H., Amin, S., Johansson, K.: Cyberphysical security in networked control systems: an introduction to the issue. IEEE Control Syst. 35(1), 20–23 (2015)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Sgouras, K., Birda, A., Labridis, D.: Cyber attack impact on critical smart grid infrastructures. In: 2014 IEEE PES Innovative Smart Grid Technologies Conference (ISGT), pp. 1–5, February 2014Google Scholar
  17. 17.
    Sridhar, S., Govindarasu, M.: Model-based attack detection and mitigation for automatic generation control. IEEE Trans. Smart Grid 5(2), 580–591 (2014)CrossRefGoogle Scholar
  18. 18.
    Symantec: Dragonfly: cyberespionage attacks against energy suppliers. Technical report (2014)Google Scholar
  19. 19.
    Zhang, L., Lampe, M., Wang, Z.: A hybrid genetic algorithm to optimize device allocation in industrial ethernet networks with real-time constraints. J. Zhejiang Univ. Sci. C 12(12), 965–975 (2011)CrossRefGoogle Scholar
  20. 20.
    Zhang, L., Lampe, M., Wang, Z.: Multi-objective topology design of industrial ethernet networks. Frequenz 66(5–6), 159–165 (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.“Petru Maior” University of Tîrgu MureşTîrgu MureşRomania

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