Abstract
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.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Power systems test case archive. https://www.ee.washington.edu/research/pstca
Abur, A., Expósito, A.: Power System State Estimation: Theory and Implementation. Power Engineering (Willis). CRC Press, Boca Raton (2004)
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)
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)
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)
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)
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). Elsevier
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)
Candès, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? J. ACM 58(3), 11:1–11:37 (2011)
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 2011
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)
Jokar, P., Arianpoo, N., Leung, V.: Intrusion detection in advanced metering infrastructure based on consumption pattern. In: IEEE International Conference on Communications (ICC), June 2013
Kim, J., Tong, L., Thomas, R.: Data framing attack on state estimation. IEEE J. Sel. Areas Commun. 32(7), 1460–1470 (2014)
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)
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 2010
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)
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)
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)
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)
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)
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)
Queiroz, C., Mahmood, A., Tari, Z.: SCADASim a framework for building scada simulations. IEEE Trans. Smart Grid 2(4), 589–597 (2011)
Rahman, M., Mohsenian-Rad, H.: False data injection attacks with incomplete information against smart power grids. In: IEEE Global Communications Conference (GLOBECOM), December 2012
Valenzuela, J., Wang, J., Bissinger, N.: Real-time intrusion detection in power system operations. IEEE Trans. Power Syst. 28(2), 1052–1062 (2013)
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 2010
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Anwar, A., Mahmood, A.N., Pickering, M. (2016). Data-Driven Stealthy Injection Attacks on Smart Grid with Incomplete Measurements. In: Chau, M., Wang, G., Chen, H. (eds) Intelligence and Security Informatics. PAISI 2016. Lecture Notes in Computer Science(), vol 9650. Springer, Cham. https://doi.org/10.1007/978-3-319-31863-9_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-31863-9_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-31862-2
Online ISBN: 978-3-319-31863-9
eBook Packages: Computer ScienceComputer Science (R0)