Liu, L., Esmalifalak, M., Ding, Q., Emesih, V., Han, Z.: Detecting false data injection attacks on power grid by sparse optimization. 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. Lin, Z., Chen, M., Ma, Y.: The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. In: International Conference on Smart Grid Communications, October 2010 Kosut, O., Jia, L., Thomas, R., Tong, L.: Malicious data attacks on smart grid state estimation: attack strategies and countermeasures. Kim, J., Tong, L., Thomas, R.: Subspace methods for data attack on state estimation: a data driven approach. Kim, J., Tong, L., Thomas, R.: Data framing attack on state estimation. In: IEEE International Conference on Communications (ICC), June 2013 Jokar, P., Arianpoo, N., Leung, V.: Intrusion detection in advanced metering infrastructure based on consumption pattern. Hug, G., Giampapa, J.: Vulnerability assessment of ac state estimation with respect to false data injection cyber-attacks. In: International Conference on Smart Grid Communications, October 2011 ACM 58(3), 11:1–11:37 (2011)Įsmalifalak, M., Nguyen, H., Zheng, R., Han, Z.: Stealth false data injection using independent component analysis in smart grid. Elsevierīi, S., Zhang, Y.J.: Graphical methods for defense against false-data injection attacks on power system state estimation. Springer International Publishing, Switzerland (2015)Īnwar, A., Mahmood, A.N., Tari, Z.: Identification of vulnerable node clusters against false data injection attack in an AMI based smart grid. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, pp. (eds.) International Conference on Security and Privacy in Communication Networks. 133, 51–62 (2016)Īnwar, A., Mahmood, A.N., Ahmed, M.: False data injection attack targeting the LTC transformers to disrupt smart grid operation. Springer, Singapore (2014)Īnwar, A., Mahmood, A.N.: Anomaly detection in electric network database of smart grid: graph matching approach. CRC Press, Taylor & Francis Group, Boca Raton, Florida (2014)Īnwar, A.: Vulnerabilities of smart grid state estimation against false data injection attack. (ed.) The State of the Art in Intrusion Prevention and Detection, pp. CRC Press, Boca Raton (2004)Īnwar, A., Mahmood, A.: Cyber security of smart grid infrastructure. KeywordsĪbur, A., Expósito, A.: Power System State Estimation: Theory and Implementation. IEEE benchmark test systems, different attack scenarios and state-of-the-art detection techniques are considered to validate the proposed claims. These attacks remain hidden in the existing bad data detection modules and affect the operation of the physical energy grid. We illustrate an attack example using augmented lagrange multiplier (ALM) method approach. 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. 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 show that an attacker can construct stealthy attacks using only the subspace information of the measurement signals without requiring any prior power system knowledge. 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. Key smart grid operational module like state estimator is highly vulnerable to a class of data integrity attacks known as ‘False Data Injection (FDI)’.
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