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On the security of distributed power system state estimation under targeted attacks

Published:18 March 2013Publication History

ABSTRACT

State estimation plays an essential role in the monitoring and control of power transmission systems. In modern, highly inter-connected power systems the state estimation should be performed in a distributed fashion and requires information exchange between the control centers of directly connected systems. Motivated by recent reports on trojans targeting industrial control systems, in this paper we investigate how a single compromised control center can affect the outcome of distributed state estimation. We describe five attack strategies, and evaluate their impact on the IEEE 118 benchmark power system. We show that that even if the state estimation converges despite the attack, the estimate can have up to 30% of error, and bad data detection cannot locate the attack. We also show that if powerful enough, the attack can impede the convergence of the state estimation, and thus it can blind the system operators. Our results show that it is important to provide confidentiality for the measurement data in order to prevent the most powerful attacks. Finally, we discuss a possible way to detect and to mitigate these attacks.

References

  1. A. Abur and A. G. Exposito. Power System State Estimation: Theory and Implementation. Marcel Dekker, Inc., 2004.Google ScholarGoogle Scholar
  2. S. d. T. Antonio J. Conejo and M. Canas. An optimization approach to multiarea state estimation. IEEE Transactions on Power Systems, 22(1), February 2007.Google ScholarGoogle ScholarCross RefCross Ref
  3. R. B. Bobba, K. M. Rogers, Q. Wang, H. Khurana, K. Nahrstedt, and T. J. Overbye. Detecting false data injection attacks on dc state estimation. In Preprints of the First Workshop on Secure Control Systems, CPSWEEK, 2010.Google ScholarGoogle Scholar
  4. S. Boyd and V. Lieven. Convex Optimization. Cambridge University Press, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. D.-H. Choi and L. Xie. Fully distributed bad data processing for wide area state estimation. In Proc. of IEEE SmartGridComm, October 2011.Google ScholarGoogle ScholarCross RefCross Ref
  6. G. Dán and H. Sandberg. Stealth attacks and protection schemes for state estimators in power systems. In Proc. of IEEE SmartGridComm, Oct. 2010.Google ScholarGoogle ScholarCross RefCross Ref
  7. T. Dierks and E. Rescorla. RFC5246: The transport layer security (TLS) protocol version 1.2. http://tools.ietf.org/html/rfc5246, August 2008.Google ScholarGoogle Scholar
  8. A. Giani, E. Bitar, M. Garcia, M. McQueen, P. Khargonekar, and K. Poolla. Smart grid data integrity attacks: Characterizations and countermeasures. In Proc. of IEEE SmartGridComm, Oct. 2011.Google ScholarGoogle Scholar
  9. R. Horn and C. R. Johnson. Matrix Analysis. Cambridge University Press, 1990. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. T. T. Kim and H. V. Poor. Strategic protection against data injection attacks on power grids. IEEE Trans. on Smart Grid, 2:326--333, Jun. 2011.Google ScholarGoogle ScholarCross RefCross Ref
  11. O. Kosut, L. Jia, R. Thomas, and L. Tong. Malicious data attacks on smart grid state estimation: Attack strategies and countermeasures. In Proc. of IEEE SmartGridComm, Oct. 2010.Google ScholarGoogle ScholarCross RefCross Ref
  12. S. K. Le Xie, Dae-Hyun Choi and H. V. Poor. Fully distributed state estimation for wide-area monitoring systems. IEEE Transactions on Smart Grid, 3(3), 2012.Google ScholarGoogle Scholar
  13. Y. Liu, P. Ning, and M. Reiter. False data injection attacks against state estimation in electric power grids. In Proc. of the 16th ACM conference on Computer and Communications Security (CCS), pages 21--32, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. A. Monticelli. Electric power system state estimation. Proc. of the IEEE, 88(2):262--282, 2000.Google ScholarGoogle ScholarCross RefCross Ref
  15. L. Sankar, S. Kar, R. Tandon, and H. V. Poor. Competitive privacy in the smart grid: An information-theoretic approach. In Proc. of IEEE SmartGridComm, Oct. 2011.Google ScholarGoogle ScholarCross RefCross Ref
  16. M. Shahidehpour and Y. Wang. Communication and Control in Electric Power Systems. John Wiley and Sons, 2003.Google ScholarGoogle ScholarCross RefCross Ref
  17. Symantec Security Response. W32.duq: The precursor to the next stuxnet, November 2011.Google ScholarGoogle Scholar
  18. A. Teixeira, G. Dán, H. Sandberg, and K. H. Johansson. A cyber security study of a SCADA energy management system: Stealthy deception attacks on the state estimator. In Proc. IFAC World Congress, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  19. O. Vuković, K. C. Sou, G. Dán, and H. Sandberg. Network-aware mitigation of data integrity attacks on power system state estimation. IEEE JSAC: Smart Grid Communications Series, 30(6), 2012.Google ScholarGoogle Scholar

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  • Published in

    cover image ACM Conferences
    SAC '13: Proceedings of the 28th Annual ACM Symposium on Applied Computing
    March 2013
    2124 pages
    ISBN:9781450316569
    DOI:10.1145/2480362

    Copyright © 2013 ACM

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 18 March 2013

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    SAC '13 Paper Acceptance Rate255of1,063submissions,24%Overall Acceptance Rate1,650of6,669submissions,25%

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