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Data Integrity Attack Detection for Node Voltage in Cyber-Physical Power System

  • Research Article-Computer Engineering and Computer Science
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Abstract

In order to accurately detect the false data injection attack for node voltage in the cyber-physical power system, an attack detection method based on recurrent neural network (RNN) is proposed in this paper. The features of voltage data of power topology nodes and the construction method of attack vector are studied. Based on the fast regression algorithm, the best strategy for the data integrity attack of the specific node voltage is solved. The RNN is used to reconstruct time series of the node voltage, and set the threshold of error between the input data and the output data. Then, by calculating whether the error between the reconstructed output data and the original input data exceeds the threshold, it is determined whether the system has suffered data integrity attack. Finally, the feasibility and effectiveness of the detection method proposed in this paper were verified by simulated attack experiments.

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References

  1. Hui, L.: Energy internet. China Electr. Power Educ. 10, 90–90 (2014)

    Google Scholar 

  2. Cardenas, A.A.; Amin, S.; Sastry, S.: Secure control: towards survivable cyber-physical systems. In: The 28th IEEE International Conference on Distributed Computing Systems Workshops, pp. 495–500. IEEE, Bei**g (2008)

  3. Rajkumar, R.; Lee, I.; Sha, L.; Stankovic, J.: Cyber-physical systems: the next computing revolution. In: 47th IEEE Design Automation Conference, pp. 731–736. IEEE, Anaheim (2010)

  4. Wang, G.; Zhang, X.; Guo, Z.: Monitoring and analysis of electric power information security. Power Syst. Technol. 28(9), 50–53 (2004)

    Google Scholar 

  5. Li, W.W.; You, W.X.; Wang, X.P.: Survey of cyber security research in power system. Power Syst. Protect. Control 39(10), 140–147 (2011)

    Google Scholar 

  6. Wen, Y.: Monitoring and analysis of electric power information security. Telecom World 8, 139–140 (2017)

    Google Scholar 

  7. Musleh, A.S.; Chen, G.; Dong, Z.Y.: A survey on the detection algorithms for false data injection attacks in smart grids. IEEE Trans. Smart Grid 11(3), 2218–2234 (2019)

    Article  Google Scholar 

  8. 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)

    Article  Google Scholar 

  9. Deng, R.; Zhuang, P.; Liang, H.: False data injection attacks against state estimation in power distribution systems. IEEE Trans. Smart Grid 10(3), 2871–2881 (2018)

    Article  Google Scholar 

  10. Chen, Y.; Huang, S.; Liu, F.; Wang, Z.S.; Sun, X.W.: Evaluation of reinforcement learning-based false data injection attack to automatic voltage control. IEEE Trans. Smart Grid 10(2), 2158–2169 (2018)

    Article  Google Scholar 

  11. Liu, X.; Song, Y.; Li, Z.: Dummy data attacks in power systems. IEEE Trans. Smart Grid. 11(2), 1792–1795 (2020)

    Article  Google Scholar 

  12. Chin, W.L.; Lee, C.H.; Jiang, T.: Blind false data attacks against ac state estimation based on geometric approach in smart grid communications. IEEE Trans. Smart Grid 9(6), 6298–6306 (2017)

    Article  Google Scholar 

  13. Zhao, J.; Mili, L.; Wang, M.: A generalized false data injection attacks against power system nonlinear state estimator and countermeasures. IEEE Trans. Power Syst. 33(5), 4868–4877 (2018)

    Article  Google Scholar 

  14. Li, B.; Ding, T.; Huang, C.; Ding, T.; Huang, C.; Zhao, J.B.; Yang, Y.H.; Chen, Y.: Detecting false data injection attacks against power system state estimation with fast go-decomposition Approach. IEEE Trans. Ind. Inf. 15(5), 2892–2904 (2018)

    Article  Google Scholar 

  15. Pan, K.; Teixeira, A.; Cvetkovic, M.; Palensky, P.: Cyber risk analysis of combined data attacks against power system state estimation. IEEE Trans. Smart Grid 10(3), 3044–3056 (2018)

    Article  Google Scholar 

  16. Wang, H.; Ruan, J.; Zhou, B.; Li, C.B.; Wu, Q.W.; Raza, M.Q.; Cao, G.Z.: Dynamic data injection attack detection of cyber physical power systems with uncertainties. IEEE Trans. Ind. Inf. 15(10), 5505–5518 (2019)

    Article  Google Scholar 

  17. Wang, X.Y.; Luo, X.Y.; Zhang, M.Y.; Jiang, Z.P.; Guan, X.P.: Detection and isolation of false data injection attacks in smart grid via unknown input interval observer. IEEE Internet Things J. 7(4), 3214–3229 (2020)

    Article  Google Scholar 

  18. Huang, Y.; Esmalifalak, M.; Nguyen, H.; Zheng, R.; Han, Z.; Li, H.; Song, L.: Bad data injection in smart grid: attack and defense mechanisms. IEEE Commun. Mag. 51(1), 27–33 (2013)

    Article  Google Scholar 

  19. Kim, T.T.; Poor, H.V.: Strategic protection against data injection attacks on power grids. IEEE Trans. Smart Grid 2(2), 326–333 (2011)

    Article  Google Scholar 

  20. Saraswathi, S.; Sundaram, S.; Sundararajan, N.; Zimmermann, M.; Nilsen-Hamilton, M.: ICGA-PSO-ELM approach for accurate multiclass cancer classification resulting in reduced gene sets in which genes encoding secreted proteins are highly represented. IEEE/ACM Trans. Comput. Biol. Bioinf. 8(2), 452–463 (2011)

    Article  Google Scholar 

  21. Liu, X.; Li, Z.: Local load redistribution attacks in power systems with incomplete network information. IEEE Trans Smart Grid 5(4), 1665–1676 (2014)

    Article  Google Scholar 

  22. Chen, P.Y.; Yang, S.; Mccann, J.A.; Lin, J.; Yang, X.: Detection of false data injection attacks in smart-grid systems. IEEE Commun. Mag. 53(2), 206–213 (2015)

    Article  Google Scholar 

  23. Yang, Q.; Jie, Y.; Yu, W.; An, D.; Zhang, N.; Zhao, W.: On false data-injection attacks against power system state estimation: modeling and countermeasures. IEEE Trans. Parallel Distrib. Syst. 25(3), 717–729 (2014)

    Article  Google Scholar 

  24. Giani, A.; Bitar, E.; Garcia, M.; McQueen, M.; Khargonekar, P.; Poolla, K.: Smart grid data integrity attacks. IEEE Trans. Smart Grid 4(3), 1244–1253 (2013)

    Article  Google Scholar 

  25. Sou, K.C.; Sandberg, H.; Johansson, K.H.: On the exact solution to a smart grid cyber-security analysis problem. IEEE Trans. Smart Grid 4(2), 856–865 (2011)

    Article  Google Scholar 

  26. Khanna, K.; Panigrahi, B.K.; Joshi, A.: AI-based approach to identify compromised meters in data integrity attacks on smart grid. IET Gener. Transm. Distrib. 12(5), 1052–1066 (2017)

    Article  Google Scholar 

  27. Esmalifalak, M.; Liu, L.; Nguyen, N.; Zhu, H.; Zheng, R.: Detecting stealthy false data injection using machine learning in smart grid. IEEE Syst. J. 11(3), 1644–1652 (2017)

    Article  Google Scholar 

  28. Ashrafuzzaman, M.; Chakhchoukh, Y.; Jillepalli, A.A.; Tosic, P.T.; Leon, D.C.D.; Sheldon, F.T.; Johnson, B.K.: Detecting stealthy false data injection attacks in power grids using deep learning. In: 14th International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 219–225. IEEE, Limassol (2018)

  29. Li, Y.; Wang, Y.; Hu, S.: Online generative adversary network based measurement recovery in false data injection attacks: a cyber-physical approach. IEEE Trans. Ind. Inf. 16(3), 2031–2043 (2020)

    Article  Google Scholar 

  30. Drayer, E.; Routtenberg, T.: Detection of false data injection attacks in smart grids based on graph signal processing. IEEE Syst. J. 14(2), 1886–1896 (2020)

    Article  Google Scholar 

  31. Abusorrah, A.; Alabdulwahab, A.; Li, Z.Y.; Shahidehpour, M.: Minimax-regret robust defensive strategy against false data injection attacks. IEEE Trans. Smart Grid 10(2), 2068–2079 (2017)

    Article  Google Scholar 

  32. Jie, S.; **lng, S.; Youkuan, K.; Shi, L.: Research on AVC system optimization of power plant based on human-simulated intelligent control. Power Syst. Protect. Control 2, 157–162 (2018)

    Google Scholar 

  33. Wang, D.; Chen, C.; Yan, J.; Guo, L.; Lai, F.: Pondering a new generation security architecture model for power information network. Autom. Electr. Power Syst. 40(2), 6–11 (2016)

    Google Scholar 

  34. North American Electric Reliability Council: (Revised) Implementation plan for cyber security standards CIP-002-1 through CIP-009-1[ROL] (2011-03-16). http://www.nerc.tom/fileUploads/File/Standards/Revised_Implementation-Plan_Clp-002-009.pdf

  35. Dajun, D.; Minrui, F.; Lixiong, L.: Radial-basis-function neural network based on fast recursive algorithm and its application. Control Theory Appl. 25(5), 827–830 (2008)

    MATH  Google Scholar 

  36. Fengming, Z.: Time Series Anomaly Detection Based on Deep Learning Theory and Smart Grid Time Series Data. Bei**g University of Posts and Telecommunications, Bei**g (2018)

    Google Scholar 

  37. Liyuan, B.; Sai, W.; Gang, C.; Lidan, S.; Ke, C.; Tianlei, H.: Database query cost prediction using recurrent neural network. J. Softw. 29(3), 799–810 (2018)

    Google Scholar 

  38. Yi, L.; Depei, B.; Zehong, Y.; Yannan, Z.; Peifa, J.; Jiaqin, W.: Research of new similarity measure method on time series data. Appl. Res. Comput. 24(5), 112–114 (2007)

    Google Scholar 

  39. Zimmerman, R.D.; Murillo-Sànchez, 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 (2010)

    Article  Google Scholar 

  40. Kekatos, V.; Giannakis, G.B.; Baldick, R.: Grid topology identification using electricity prices. In: IEEE PES General Meeting| Conference & Exposition, pp. 1–5. IEEE, National Harbor (2014)

  41. Zhao, J.B.; Zhang, G.X.; Scala, M.L.; Zhao, Y.D.; Chen, C.; Wang, J.H.: Short-term state forecasting-aided method for detection of smart grid general false data injection attacks. IEEE Trans. Smart Grid 8(4), 1580–1590 (2015)

    Article  Google Scholar 

  42. Lütkepohl, H.: New introduction to multiple time series analysis. Econ. Rec. 83(260), 109–110 (2007)

    Article  Google Scholar 

  43. Hassanzadeh, M.; Evrenosoğlu, C.Y.; Mili, L.: A short-term nodal voltage phasor forecasting method using temporal and spatial correlation. IEEE Trans. Power Syst. 31(5), 3881–3890 (2015)

    Article  Google Scholar 

Download references

Acknowledgements

Funding was provided by National Natural Science Foundation of China (Grant No. 61972148).

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Authors and Affiliations

  1. School of Control and Computer Engineering, North China Electric Power University, Bei**g, China

    Ruzhi Xu, Dawei Chen & Rui Wang

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  1. Ruzhi Xu
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  2. Dawei Chen
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  3. Rui Wang
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Correspondence to Dawei Chen.

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Xu, R., Chen, D. & Wang, R. Data Integrity Attack Detection for Node Voltage in Cyber-Physical Power System. Arab J Sci Eng 45, 10591–10603 (2020). https://doi.org/10.1007/s13369-020-04813-y

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  • DOI: https://doi.org/10.1007/s13369-020-04813-y

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