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Data-driven attacks and data recovery with noise on state estimation of smart grid. (English) Zbl 1459.94004
Summary: In this paper, we focus on the false data injection attacks (FDIAs) on state estimation and corresponding countermeasures for data recovery in smart grid. Without the information about the topology and parameters of systems, two data-driven attacks (DDAs) with noisy measurements are constructed, which can escape the detection from the residue-based bad data detection (BDD) in state estimator. Moreover, in view of the limited energy of adversaries, the feasibility of proposed DDAs is improved, such as more sparse and low-cost DDAs than existing work. In addition, a new algorithm for measurement data recovery is introduced, which converts the data recovery problem against the DDAs into the problem of the low rank approximation with corrupted and noisy measurements. Especially, the online low rank approximate algorithm is employed to improve the real-time performance. Finally, the information on the 14-bus power system is employed to complete the simulation experiments. The results show that the constructed DDAs are stealthy under BBD but can be eliminated by the proposed data recovery algorithms, which improve the resilience of the state estimator against the attacks.
MSC:
94A05 Communication theory
68W27 Online algorithms; streaming algorithms
Software:
LMaFit; MATPOWER
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[1] Liang G, L. F.e.a., Bibtex: a review of false data injection attacks against modern power systems, IEEE Trans. Smart Grid, 8, 4, 1630-1638 (2017)
[2] Cao, X.; Liu, L.; Shen, W.; Laha, A.; Tang, J.; Cheng, Y., Real-time misbehavior detection and mitigation in cyber-physical systems over wlans, IEEE Trans. Ind. Inf., 13, 1, 186-197 (2017)
[3] Liang J, K. O.; Sankar, L., Bibtex: Vulnerability analysis and consequences of false data injection attack on power system state estimation, IEEE Trans. Power Syst., 31, 5, 3864-3872 (2016)
[4] Fawzi, H.; Tabuada, P.; Diggavi, S., Secure estimation and control for cyber-physical systems under adversarial attacks, IEEE Trans. Autom. Control, 59, 6, 1454-1467 (2014) · Zbl 1360.93201
[5] Li, Q.; Xu, B.; Li, S.; Liu, Y.; Cui, D., Reconstruction of measurements in state estimation strategy against deception attacks for cyber physical systems, Control Theory Technol., 16, 1, 1-13 (2018)
[6] Liu Y, R. M.K.; Ning, P., False data injection attacks against state estimation in electric power grids, ACM Trans. Inf. Syst. Secur. (TISSEC), 14, 1, 13 (2011)
[7] Rahman, M. A.; Mohsenian-Rad, H., False data injection attacks with incomplete information against smart power grids, Global Communications Conference (GLOBECOM), 2012 IEEE, 3153-3158 (2012), IEEE
[8] Liu, X.; Bao, Z.; Lu, D.; Li, Z., Modeling of local false data injection attacks with reduced network information, IEEE Trans. Smart Grid, 6, 4, 1686-1696 (2017)
[9] Esmalifalak, M.; Nguyen, H.; Zheng, R.; Han, Z., Stealth false data injection using independent component analysis in smart grid, Proceedings of the IEEE International Conference on Smart Grid Communications, 244-248 (2011)
[10] 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)
[11] Kim, J.; Tong, L.; Thomas, R. J., Subspace methods for data attack on state estimation: A data driven approach, IEEE Trans. Signal Process., 63, 5, 1102-1114 (2015) · Zbl 1394.94276
[12] Zhang, H.; Cheng, P.; Shi, L.; Chen, J., Optimal denial-of-service attack scheduling with energy constraint, IEEE Trans. Autom. Control, 60, 11, 3023-3028 (2015) · Zbl 1360.68302
[13] Hao, J.; Piechocki, R. J.; Kaleshi, D.; Chin, W. H.; Fan, Z., Sparse malicious false data injection attacks and defense mechanisms in smart grids, IEEE Trans. Ind. Inf., 11, 5, 1-12 (2017)
[14] Deng, R.; Xiao, G.; Lu, R., Defending against false data injection attacks on power system state estimation, IEEE Trans. Ind. Inf., 13, 1, 198-207 (2017)
[15] Yang, Q.; Yang, J.; Yu, W.; An, D.; Zhang, N.; Zhao, W., On false data-injection attacks against power system state estimation: modeling and countermeasures, IEEE Trans. Parallel Distr. Syst., 25, 3, 717-729 (2014)
[16] Pasqualetti, F.; Dörfler, F.; Bullo, F., Attack detection and identification in cyber-physical systems, IEEE Trans. Autom. Control, 58, 11, 2715-2729 (2013) · Zbl 1369.93675
[17] Tan, S.; Song, W. Z.; Stewart, M.; Yang, J.; Tong, L., Online data integrity attacks against real-time electrical market in smart grid, IEEE Trans. Smart Grid, 9, 1, 313-322 (2018)
[18] Chong, M. S.; Wakaiki, M.; Hespanha, J. P., Observability of linear systems under adversarial attacks, Proceedings of the American Control Conference, 2439-2444 (2015)
[19] 1-1 doi:10.1109/tcns.2017.2704434.
[20] Wei, A.; Song, Y.; Wen, C., Adaptive cyber-physical system attack detection and reconstruction with application to power systems, Iet Control Theory Appl., 10, 12, 1458-1468 (2016)
[21] Shoukry, Y.; Tabuada, P., Event-triggered state observers for sparse sensor noise/attacks, IEEE Trans. Autom. Control, 61, 8, 2079-2091 (2016) · Zbl 1359.93072
[22] Sid M. A, C. K., Medium access scheduling for input reconstruction under deception attacks, J. Frankl. Inst., 354, 9, 3678-3689 (2017) · Zbl 1367.93662
[23] Lin, Z.; Chen, M.; Ma, Y., The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices (2010), arXiv preprint arXiv:1009.5055
[24] Liu, L.; Esmalifalak, M.; Ding, Q.; Emesih, V. A.; Han, Z., Detecting false data injection attacks on power grid by sparse optimization, IEEE Trans. Smart Grid, 5, 2, 612-621 (2014)
[25] Shen, Y.; Wen, Z.; Zhang, Y., Augmented lagrangian alternating direction method for matrix separation based on low-rank factorization, Optim. Methods Softw., 29, 2, 239-263 (2014) · Zbl 1285.90068
[26] Gomez-Exposito, A.; Abur, A., Power system state estimation: theory and implementation (2004), CRC press
[27] Minot, A.; Li, N., A fully distributed state estimation using matrix splitting methods, Proceedings of the American Control Conference, 2488-2493 (2015)
[28] Ozay, M.; Esnaola, I.; Vural, F. T.Y.; Kulkarni, S. R.; Poor, H. V., Sparse attack construction and state estimation in the smart grid: Centralized and distributed models, IEEE J. Select. Areas Commun., 31, 7, 1306-1318 (2013)
[29] 1-1 doi:10.1109/tac.2017.2775344.
[30] Zhang, H.; Zheng, W. X., Denial-of-service power dispatch against linear quadratic control via a fading channel, IEEE Trans. Autom. Control (2018) · Zbl 1423.90164
[31] Kutyniok, G., Theory and applications of compressed sensing, 36, 79-101 (2013), Gamm-Mitteilungen · Zbl 1283.94018
[32] 1-122 doi:10.1561/2200000016. · Zbl 1229.90122
[33] Huang, H.; Yan, Q.; Zhao, Y.; Lu, W.; Liu, Z.; Li, Z., False data separation for data security in smart grids, Know. Inf. Syst., 52, 3, 815-834 (2017)
[34] Zhou, T.; Tao, D., Godec: randomized low-rank and sparse matrix decomposition in noisy case, Proceedings of the International Conference on Machine Learning (2011), Omnipress
[35] Zhou, T.; Tao, D., Greedy bilateral sketch, completion and smoothing, Proceedings of the International Conference on Artificial Intelligence and Statistics (2013), JMLR. org
[36] Zimmerman, R. D.; Murillo-Sanchez, C. E.; Thomas, R. J., Matpower: Steady-state operations, planning, and analysis tools for power systems research and education, IEEE Trans. Power Syst., 26, 1, 12-19 (2011)
[37] Yan, H.; Zhang, H.; Yang, F.; Zhan, X.; Peng, C., Event-triggered asynchronous guaranteed cost control for Markov jump discrete-time neural networks with distributed delay and channel fading, IEEE Trans. Neural Netw. Learn. Syst., PP, 99, 1-11 (2017)
[38] 1-1 doi:10.1109/TAC.2018.2810514. · Zbl 1423.93147
[39] Liu, J.; Xia, J.; Tian, E.; Fei, S., Hybrid-driven-based h∞ filter design for neural networks subject to deception attacks, Appl. Math. Comput., 320, 158-174 (2018) · Zbl 1426.93086
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