Data-Driven Stealthy Injection Attacks on Smart Grid with Incomplete Measurements

  • Adnan AnwarEmail author
  • Abdun Naser Mahmood
  • Mark Pickering
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9650)


Key smart grid operational module like state estimator is highly vulnerable to a class of data integrity attacks known as ‘False Data Injection (FDI)’. Although most of the existing FDI attack construction strategies require the knowledge of the power system topology and electric parameters (e.g., line resistance and reactance), this paper proposes an alternative data-driven approach. We show that an attacker can construct stealthy attacks using only the subspace information of the measurement signals without requiring any prior power system knowledge. However, principle component analysis (PCA) or singular value decomposition (SVD) based attack construction techniques do not remain stealthy if measurement signals contain missing values. We demonstrate that even in that case an intelligent attacker is able to construct the stealthy FDI attacks using low-rank and sparse matrix approximation techniques. We illustrate an attack example using augmented lagrange multiplier (ALM) method approach. These attacks remain hidden in the existing bad data detection modules and affect the operation of the physical energy grid. IEEE benchmark test systems, different attack scenarios and state-of-the-art detection techniques are considered to validate the proposed claims.


False injection Smart grid State estimator Blind attack PCA SCADA EMS 


  1. 1.
    Power systems test case archive.
  2. 2.
    Abur, A., Expósito, A.: Power System State Estimation: Theory and Implementation. Power Engineering (Willis). CRC Press, Boca Raton (2004)CrossRefGoogle Scholar
  3. 3.
    Anwar, A., Mahmood, A.: Cyber security of smart grid infrastructure. In: Pathan, A.-S.K. (ed.) The State of the Art in Intrusion Prevention and Detection, pp. 139–154. CRC Press, Taylor & Francis Group, Boca Raton, Florida (2014)CrossRefGoogle Scholar
  4. 4.
    Anwar, A.: Vulnerabilities of smart grid state estimation against false data injection attack. In: Hossain, J., Mahmud, A. (eds.) Renewable Energy Integration. Green Energy and Technology, pp. 411–428. Springer, Singapore (2014)CrossRefGoogle Scholar
  5. 5.
    Anwar, A., Mahmood, A.N.: Anomaly detection in electric network database of smart grid: graph matching approach. Electr. Power Syst. Res. 133, 51–62 (2016)CrossRefGoogle Scholar
  6. 6.
    Anwar, A., Mahmood, A.N., Ahmed, M.: False data injection attack targeting the LTC transformers to disrupt smart grid operation. In: Tian, J., Jing, J., Srivatsa, M. (eds.) International Conference on Security and Privacy in Communication Networks. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, pp. 252–266. Springer International Publishing, Switzerland (2015)CrossRefGoogle Scholar
  7. 7.
    Anwar, A., Mahmood, A.N., Tari, Z.: Identification of vulnerable node clusters against false data injection attack in an AMI based smart grid. Inf. Syst. 53, 201–212 (2015). ElsevierCrossRefGoogle Scholar
  8. 8.
    Bi, S., Zhang, Y.J.: Graphical methods for defense against false-data injection attacks on power system state estimation. IEEE Trans. Smart Grid 5(3), 1216–1227 (2014)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Candès, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? J. ACM 58(3), 11:1–11:37 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Esmalifalak, M., Nguyen, H., Zheng, R., Han, Z.: Stealth false data injection using independent component analysis in smart grid. In: International Conference on Smart Grid Communications, October 2011Google Scholar
  11. 11.
    Hug, G., Giampapa, J.: Vulnerability assessment of ac state estimation with respect to false data injection cyber-attacks. IEEE Trans. Smart Grid 3(3), 1362–1370 (2012)CrossRefGoogle Scholar
  12. 12.
    Jokar, P., Arianpoo, N., Leung, V.: Intrusion detection in advanced metering infrastructure based on consumption pattern. In: IEEE International Conference on Communications (ICC), June 2013Google Scholar
  13. 13.
    Kim, J., Tong, L., Thomas, R.: Data framing attack on state estimation. IEEE J. Sel. Areas Commun. 32(7), 1460–1470 (2014)CrossRefGoogle Scholar
  14. 14.
    Kim, J., Tong, L., Thomas, R.: Subspace methods for data attack on state estimation: a data driven approach. IEEE Trans. Sign. Process. 63(5), 1102–1114 (2015)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Kosut, O., Jia, L., Thomas, R., Tong, L.: Malicious data attacks on smart grid state estimation: attack strategies and countermeasures. In: International Conference on Smart Grid Communications, October 2010Google Scholar
  16. 16.
    Lin, Z., Chen, M., Ma, Y.: The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. Technical report, UIUC Technical report UILU-ENG-09-2214 (2009)Google Scholar
  17. 17.
    Lin, Z., Chen, M., Ma, Y.: Fast convex optimization algorithms for exact recovery of a corrupted low-rank matrix. Technical report, UIUC Technical report UILU-ENG-09-2214 (2009)Google Scholar
  18. 18.
    Liu, L., Esmalifalak, M., Ding, Q., Emesih, V., Han, Z.: Detecting false data injection attacks on power grid by sparse optimization. IEEE Trans. Smart Grid 5(2), 612–621 (2014)CrossRefGoogle Scholar
  19. 19.
    Liu, Y., Ning, P., Reiter, M.K.: False data injection attacks against state estimation in electric power grids. In: Proceedings of the 16th ACM Conference on Computer and Communications Security, CCS 2009, pp. 21–32. ACM, New York (2009)Google Scholar
  20. 20.
    Liu, Y., Ning, P., Reiter, M.K.: False data injection attacks against state estimation in electric power grids. ACM Trans. Inf. Syst. Secur. 14(1), 13:1–13:33 (2011)CrossRefGoogle Scholar
  21. 21.
    Ozay, M., Esnaola, I., Vural, F., Kulkarni, S., Poor, H.: Sparse attack construction and state estimation in the smart grid: centralized and distributed models. IEEE J. Sel. Areas Commun. 31(7), 1306–1318 (2013)CrossRefGoogle Scholar
  22. 22.
    Queiroz, C., Mahmood, A., Tari, Z.: SCADASim a framework for building scada simulations. IEEE Trans. Smart Grid 2(4), 589–597 (2011)CrossRefGoogle Scholar
  23. 23.
    Rahman, M., Mohsenian-Rad, H.: False data injection attacks with incomplete information against smart power grids. In: IEEE Global Communications Conference (GLOBECOM), December 2012Google Scholar
  24. 24.
    Valenzuela, J., Wang, J., Bissinger, N.: Real-time intrusion detection in power system operations. IEEE Trans. Power Syst. 28(2), 1052–1062 (2013)CrossRefGoogle Scholar
  25. 25.
    Xie, L., Mo, Y., Sinopoli, B.: False data injection attacks in electricity markets. In: IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 226–231, October 2010Google Scholar
  26. 26.
    Yu, Z.-H., Chin, W.-L.: Blind false data injection attack using pca approximation method in smart grid. IEEE Trans. Smart Grid 6(3), 1219–1226 (2015)CrossRefGoogle Scholar
  27. 27.
    Zimmerman, R., Murillo-Sanchez, C., Thomas, R.: MATPOWER: steady-state operations, planning, and analysis tools for power systems research and education. IEEE Trans. Power Syst. 26(1), 12–19 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Adnan Anwar
    • 1
    Email author
  • Abdun Naser Mahmood
    • 1
  • Mark Pickering
    • 1
  1. 1.School of Engineering and Information TechnologyUNSWCanberraAustralia

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