Abstract
In the context of Industry 4.0, the high-profile false data injection (FDI) attacks are posing increasing cyber threats to the reliability of smart grids. Recent studies have investigated the possibilities of detecting FDI attacks on smart grids by using the distributed flexible AC transmission system (D-FACTS) devices. However, few studies focus on further locating such cyber threats using D-FACTS devices. To meet this gap, we systematically explored such a topic and propose a graph theory based scheme to locate FDI attacks by employing D-FACTS devices, where both single-bus FDI attacks and multiple-bus FDI attacks are considered. Numerical results on the standard IEEE 14-bus system demonstrated that the proposed scheme can achieve 100% accuracy when locating any single-bus FDI attacks and most of the independent multiple-bus FDI attacks. Future potential solutions are also discussed to some special cases of multiple-bus FDI attacks that the proposed scheme cannot well handle.
This work was supported in part by the National Key Research and Development Program of China (No. 2020YFB1805400); in part by the National Natural Science Foundation of China (No. 62002248); in part by the China Postdoctoral Science Foundation (No. 2019TQ0217 and No. 2020M673277); in part by the Provincial Key Research and Development Program of Sichuan (No. 20ZDYF3145); in part by the Fundamental Research Funds for the Central Universities; in part by the China International Postdoctoral Exchange Fellowship Program (Talent-Introduction).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abur, A., Gómez Expósito, A.: Power system state estimation: theory and implementation. Power Engineering, Marcel Dekker, New York, NY (2004). OCLC: ocm55070738
Ashok, A., Govindarasu, M., Ajjarapu, V.: Online detection of stealthy false data injection attacks in power system state estimation. IEEE Trans. Smart Grid, p. 1 (2016). https://doi.org/10.1109/TSG.2016.2596298
Deng, R., Liang, H.: False data injection attacks with limited susceptance information and new countermeasures in smart grid. IEEE Trans. Industr. Inf. 15(3), 1619–1628 (2019). https://doi.org/10.1109/TII.2018.2863256
Devanny, J., Goldoni, L.R.F., Medeiros, B.P.: The 2019 Venezuelan blackout and the consequences of cyber uncertainty. Revista Brasileira de Estudos de Defesa 7(2) (2020)
Divan, D., Johal, H.: Distributed FACTS-A new concept for realizing grid power flow control. In: 2005 IEEE 36th Power Electronics Specialists Conference, pp. 8–14 (2005). https://doi.org/10.1109/PESC.2005.1581595
Ervural, B.C., Ervural, B.: Overview of cyber security in the industry 4.0 era. In: Industry 4.0: Managing The Digital Transformation. SSAM, pp. 267–284. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-57870-5_16
Kruskal, J.B.: On the shortest spanning subtree of a graph and the traveling salesman problem. Proc. Am. Math. Soc. 7(1), 48–50 (1956)
Lakshminarayana, S., Belmega, E.V., Poor, H.V.: Moving-target defense for detecting coordinated cyber-physical attacks in power grids. In: 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), pp. 1–7. IEEE, Beijing (2019). https://doi.org/10.1109/SmartGridComm.2019.8909767
Li, B., Ding, T., Huang, C., Zhao, J., Yang, Y., Chen, Y.: Detecting false data injection attacks against power system state estimation with fast go-decomposition approach. IEEE Trans. Industr. Inf. 15(5), 2892–2904 (2019). https://doi.org/10.1109/TII.2018.2875529
Li, B., Wu, Y., Song, J., Lu, R., Li, T., Zhao, L.: DeepFed: federated deep learning for intrusion detection in industrial cyber-physical systems. IEEE Trans. Industr. Inf. 17(8), 5615–5624 (2020)
Li, B., Xiao, G., Lu, R., Deng, R., Bao, H.: On feasibility and limitations of detecting false data injection attacks on power grid state estimation using D-FACTS devices. IEEE Trans. Industr. Inf. 16(2), 854–864 (2020). https://doi.org/10.1109/TII.2019.2922215
Liang, G., Weller, S.R., Zhao, J., Luo, F., Dong, Z.Y.: The 2015 Ukraine blackout: implications for false data injection attacks. IEEE Trans. Power Syst. 32(4), 3317–3318 (2017). https://doi.org/10.1109/TPWRS.2016.2631891
Liu, B., Wu, H.: Optimal planning and operation of hidden moving target defense for maximal detection effectiveness. IEEE Trans. Smart Grid (2021). https://doi.org/10.1109/TSG.2021.3076824
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), 1–33 (2011). https://doi.org/10.1145/1952982.1952995
Mohammadpourfard, M., Sami, A., Seifi, A.R.: A statistical unsupervised method against false data injection attacks: a visualization-based approach. Expert Syst. Appl. 84, 242–261 (2017). https://doi.org/10.1016/j.eswa.2017.05.013
Morrow, K.L., Heine, E., Rogers, K.M., Bobba, R.B., Overbye, T.J.: Topology perturbation for detecting malicious data injection. In: Proceedings of the 2012 45th Hawaii International Conference on System Sciences, HICSS 2012, pp. 2104–2113. IEEE Computer Society (2012). https://doi.org/10.1109/HICSS.2012.594
Mukherjee, D., Chakraborty, S., Ghosh, S.: Deep learning-based multilabel classification for locational detection of false data injection attack in smart grids. Electr. Eng., 1–24 (2021). https://doi.org/10.1007/s00202-021-01278-6
Rahman, M.A., Al-Shaer, E., Bobba, R.B.: Moving target defense for hardening the security of the power system state estimation. In: Proceedings of the First ACM Workshop on Moving Target Defense, pp. 59–68 (2014)
Salehghaffari, H., Khorrami, F.: Resilient power grid state estimation under false data injection attacks. In: 2018 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT), pp. 1–5 (2018). https://doi.org/10.1109/ISGT.2018.8403396
Shi, W., Wang, Y., Jin, Q., Ma, J.: PDL: an efficient prediction-based false data injection attack detection and location in smart grid. In: 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), vol. 2, pp. 676–681 (2018). https://doi.org/10.1109/COMPSAC.2018.10317
Tian, J., Tan, R., Guan, X., Liu, T.: Hidden moving target defense in smart grids. In: Proceedings of the 2nd Workshop on Cyber-Physical Security and Resilience in Smart Grids, CPSR-SG 2017, pp. 21–26. Association for Computing Machinery, New York (2017). https://doi.org/10.1145/3055386.3055388
Wang, S., Bi, S., Zhang, Y.J.A.: Locational detection of the false data injection attack in a smart grid: a multilabel classification approach. IEEE Internet Things J. 7(9), 8218–8227 (2020). https://doi.org/10.1109/JIOT.2020.2983911
Zhang, Z., Deng, R., Cheng, P., Chow, M.Y.: Strategic protection against FDI attacks with moving target defense in power grids. IEEE Trans. Control Netw. Syst. (2021). https://doi.org/10.1109/TCNS.2021.3100411
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, B., Du, Q., Song, J., Li, A., Ma, X. (2021). Locating False Data Injection Attacks on Smart Grids Using D-FACTS Devices. In: Hacid, H., Kao, O., Mecella, M., Moha, N., Paik, Hy. (eds) Service-Oriented Computing. ICSOC 2021. Lecture Notes in Computer Science(), vol 13121. Springer, Cham. https://doi.org/10.1007/978-3-030-91431-8_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-91431-8_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-91430-1
Online ISBN: 978-3-030-91431-8
eBook Packages: Computer ScienceComputer Science (R0)