skip to main content
10.1145/2516930.2516947acmconferencesArticle/Chapter ViewAbstractPublication PagesccsConference Proceedingsconference-collections
research-article

Semantic security analysis of SCADA networks to detect malicious control commands in power grids

Authors Info & Claims
Published:08 November 2013Publication History

ABSTRACT

In the current generation of SCADA (Supervisory Control And Data Acquisition) systems used in power grids, a sophisticated attacker can exploit system vulnerabilities and use a legitimate maliciously crafted command to cause a wide range of system changes that traditional contingency analysis does not consider and remedial action schemes cannot handle. To detect such malicious commands, we propose a semantic analysis framework based on a distributed network of intrusion detection systems (IDSes). The framework combines system knowledge of both cyber and physical infrastructure in power grid to help IDS to estimate execution consequences of control commands, thus to reveal attacker's malicious intentions. We evaluated the approach on the IEEE 30-bus system. Our experiments demonstrate that: (i) by opening 3 transmission lines, an attacker can avoid detection by the traditional contingency analysis and instantly put the tested 30-bus system into an insecure state and (ii) the semantic analysis provides reliable detection of malicious commands with a small amount of analysis time.

References

  1. Electrical grid in U.S. penetrated by spies. The Wall Street Journal, p. A1, April 8, 2009.Google ScholarGoogle Scholar
  2. Glover, J. D., Sarma, M.S. and Overbye, T. 2011. Power System Analysis and Design, 5th ed., Cengage Learning.Google ScholarGoogle Scholar
  3. Lin, H., Slagell, A., Di Martino, C., Kalbarczyk, Z. and Iyer, R.K. Adapting Bro into SCADA: Building a specification-based intrusion detection system for the DNP3 protocol. In Proc. of 8th Annual Cyber Security and Information Intelligence Research Workshop, 2013). Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Falliere, N., Murchu, L. and Chien, E. W32.Stuxnet dossier. Symantec Security Response, 2011.Google ScholarGoogle Scholar
  5. Monticelli, A. Electric power system state estimation. In Proceedings of the IEEE, Vol.88(2), 2000.Google ScholarGoogle Scholar
  6. Prais, M. and Bose, A. A topology processor that tracks network modifications. 1998. IEEE Transactions on Power Systems (August 1988), vol. 3, no.3, pp. 992--998.Google ScholarGoogle Scholar
  7. Liu, Y., Ning, P., and Reiter M. False data injection attacks against state estimation in electric power grids. In Proceedings of the 16th ACM Conference on Computer and Communications Security, CCS'09, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Bobba, R., Rogers, K., Wang, Q., Khurana, H., Nahrstedt, K., and Overbye T. Detecting false data injection attacks on DC state estimation. In Preprints of the First Workshop on Secure Control Systems, SCS 2010.Google ScholarGoogle Scholar
  9. Open DNP3 Group. 2012. DNP3 -- Distributed Network Protocol 3.0 Google project hosting. Online. Available: http://code.google.com/p/dnp3/.Google ScholarGoogle Scholar
  10. Yang, T., Sun, H., and Bose, A. Transition to a two-level linear state estimator -- Part 1: architecture. 2011. IEEE Transactions on Power Systems, 26(1) 2011).Google ScholarGoogle ScholarCross RefCross Ref
  11. IEEE standard communication delivery time performance requirements for electric power sub-station automation. IEEE Std 1646--2004, 2005.Google ScholarGoogle Scholar
  12. Zimmerman, R. D., Murillo-Sánchez, C. E., and Thomas, R. J. MATPOWER: Steady-state operations, planning and analysis tools for power systems research and education. 2011. IEEE Transactions on Power Systems, 26(1), 2011).Google ScholarGoogle ScholarCross RefCross Ref
  13. Gutman, R., Marchenko, P., and Dunlop, R. Analytical development of loadability characteristics for EHV and UHV transmission lines. IEEE Transactions on Power Apparatus and Systems, PAS-98(2), 1979.Google ScholarGoogle ScholarCross RefCross Ref
  14. Midwest Independent Transmission System Operator, Inc. 2012. June 2012 Monthly Market Assessment Report.Google ScholarGoogle Scholar
  15. Lesieutre, B., Pinar, A., and Roy S. Power system extreme event detection: The vulnerability frontier. In Proceedings of 41st Annual Hawaii International Conference on System Sciences (January 2008). Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Carcano, A., Fovino, I., Masera, M., and Trombetta, A. State-based network intrusion detection systems for SCADA protocols: a proof of concept. Critical Information Infrastructures Security, Lecture Notes in Computer Science, vol. 6027, 2010, pp. 138--150 Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Semantic security analysis of SCADA networks to detect malicious control commands in power grids

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        SEGS '13: Proceedings of the first ACM workshop on Smart energy grid security
        November 2013
        112 pages
        ISBN:9781450324922
        DOI:10.1145/2516930

        Copyright © 2013 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 8 November 2013

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        SEGS '13 Paper Acceptance Rate12of27submissions,44%Overall Acceptance Rate19of38submissions,50%

        Upcoming Conference

        CCS '24
        ACM SIGSAC Conference on Computer and Communications Security
        October 14 - 18, 2024
        Salt Lake City , UT , USA

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader