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Locating False Data Injection Attacks on Smart Grids Using D-FACTS Devices

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Service-Oriented Computing (ICSOC 2021)

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).

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References

  1. Abur, A., Gómez Expósito, A.: Power system state estimation: theory and implementation. Power Engineering, Marcel Dekker, New York, NY (2004). OCLC: ocm55070738

    Google Scholar 

  2. 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

  3. 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

    Article  Google Scholar 

  4. 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)

    Google Scholar 

  5. 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

  6. 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

    Chapter  Google Scholar 

  7. 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)

    Article  MathSciNet  Google Scholar 

  8. 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

  9. 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

    Article  Google Scholar 

  10. 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)

    Article  Google Scholar 

  11. 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

    Article  Google Scholar 

  12. 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

    Article  Google Scholar 

  13. 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

    Article  Google Scholar 

  14. 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

    Article  Google Scholar 

  15. 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

    Article  Google Scholar 

  16. 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

  17. 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

  18. 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)

    Google Scholar 

  19. 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

  20. 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

  21. 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

  22. 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

    Article  Google Scholar 

  23. 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

    Article  Google Scholar 

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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

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  • DOI: https://doi.org/10.1007/978-3-030-91431-8_18

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  • Online ISBN: 978-3-030-91431-8

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