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Understanding Cybersecurity Challenges and Detection Algorithms for False Data Injection Attacks in Smart Grids

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Internet of Things. Advances in Information and Communication Technology (IFIPIoT 2023)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 684))

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Abstract

In Smart grid (SG), cyber-physical attacks (CPA) are the most critical hurdles to the use and development. False data injection attack (FDIA) is a main group among these threats, with a broad range of methods and consequences that have been widely documented in recent years. To overcome this challenge, several recognition processes have been developed in current years. These algorithms are mainly classified into model-based algorithms or data-driven algorithms. By categorizing these algorithms and discussing the advantages and disadvantages of each group, this analysis provides an intensive overview of them. The Chapter begins by introducing different types of CPA as well as the major stated incidents history. In addition, the chapter describes the use of Machine Learning (ML) techniques to distinguish false injection attacks in Smart Grids. A few remarks are made in the conclusion as to what should be considered when developing forthcoming recognition algorithms for fake data injection attacks.

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References

  1. Reed, T.: At the Abyss: An Insider’s History of the Cold War. Presidio Press, Novato (2005)

    Google Scholar 

  2. Hardy, T.L.: Software and System Safety: Accidents, Incidents, and Lessons Learned. AuthorHouse, Bloomington (2012)

    Google Scholar 

  3. Swearingen, M., Brunasso, S., Weiss, J., Huber, D.: What You Need to Know (and Don’t) About the AURORA Vulnerability. PowerMag (2013)

    Google Scholar 

  4. Manandhar, K., Cao, X., Hu, F., Liu, Y.: Detection of faults and attacks including false data injection attack in smart grid using Kalman filter. IEEE Trans. Control Netw. Syst. 1(4), 370–379 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  5. Esmalifalak, M., Han, Z.: Detecting stealthy false data injection using machine learning in smart grid. IEEE Syst. J. 11(3), 1644–1652 (2014)

    Article  Google Scholar 

  6. Ford, V., Siraj, A., Eberle, W.: Smart grid energy fraud detection using artificial neural networks. In: IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG), pp. 1–6. IEEE (2014)

    Google Scholar 

  7. Toppa, S.: The National Power Grid Is Under Almost Continuous Attack, Report Says, Time.com (2015). https://bit.ly/1FH246I

  8. Yan, J., Tang, B., He, H.: Detection of false data attacks in smart grid with supervised learning. In: International Joint Conference on Neural Networks (IJCNN), pp. 1395–1402. IEEE (2016)

    Google Scholar 

  9. Valdes, A., Macwan, R., Backes, M.: Anomaly detection in electrical substation circuits via unsupervised machine learning. In: IEEE 17th International Conference on Information Reuse and Integration (IRI), pp. 500–505. IEEE (2016)

    Google Scholar 

  10. Jiang, Y., Hui, Q.: Kalman filter with diffusion strategies for detecting power grid false data injection attacks. In: IEEE International Conference on Electro Information Technology (EIT) (2017)

    Google Scholar 

  11. Khalid, H.M., Peng, J.C.-H.: Immunity toward data-injection attacks using multisensor track fusion-based model prediction. IEEE Trans. Smart Grid 8(2), 697–707 (2017)

    Google Scholar 

  12. Jiang, Q., Chen, H., Xie, L., Wang, K.: Real-time detection of false data injection attack using residual prewhitening in smart grid network. In: IEEE International Conference on Smart Grid Communications (SmartGridComm) (2017)

    Google Scholar 

  13. Chung, H.-M., Li, W.-T., Yuen, C., Chung, W.-H., Wen, C.-K.: Local cyber-physical attack with leveraging detection in smart grid. In: IEEE International Conference on Smart Grid Communications (SmartGridComm) (2017)

    Google Scholar 

  14. Shi, W., Wang, Y., Jin, Q., Ma, J.: PDL: an efficient prediction-based false data injection attack detection and location in smart grid. In: IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC) (2018)

    Google Scholar 

  15. Sahoo, S., Mishra, S., Peng, J.C.-H., Dragicevic, T.: A stealth cyber attack detection strategy for DC microgrids. IEEE Trans. Power Electron. (2018, in press)

    Google Scholar 

  16. Kubo, R.: Detection and mitigation of false data injection attacks for secure interactive networked control systems. In: IEEE International Conference on Intelligence and Safety for Robotics (ISR) (2018)

    Google Scholar 

  17. Kurt, M.N., Ogundijo, O., Li, C., Wang, X.: Online cyber-attack detection in smart grid: a reinforcement learning approach. IEEE Trans. Smart Grid, 1–12 (2018, in press)

    Google Scholar 

  18. Adhikari, U., Morris, T.H., Pan, S.: Applying non-nested generalized exemplars classification for cyber-power event and intrusion detection. IEEE Trans. Smart Grid 9(5), 3928–3941 (2018)

    Article  Google Scholar 

  19. Beg, O.A., Nguyen, L.V., Johnson, T.T., Davoudi, A.: Signal temporal logic-based attack detection in DC microgrids. IEEE Trans. Smart Grid (2018, in press)

    Google Scholar 

  20. Ayad, A., Farag, H.E.Z., Youssef, A., El-Saadany, E.F.: Detection of false data injection attacks in smart grids using recurrent neural networks. In: 2018 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference (ISGT), pp. 1–5. IEEE (2018)

    Google Scholar 

  21. Zhao, J., Gómez-Expósito, A.: Power system dynamic state estimation: motivations, definitions, methodologies, and future work. IEEE Trans. Power Syst. 34(4), 3188–3198 (2019)

    Article  Google Scholar 

  22. Kurt, M.N., Yılmaz, Y., Wang, X.: Real-time detection of hybrid and stealthy cyber-attacks in smart grid. IEEE Trans. Inf. Forensics Secur. 14(2), 498–513 (2019)

    Article  Google Scholar 

  23. Zanetti, M., Jamhour, E., Chueiri, I.: A tunable fraud detection system for advanced metering infrastructure using short-lived patterns. IEEE Trans. Smart Grid 10(1), 830–840 (2019)

    Article  Google Scholar 

  24. Karimipour, H., Choo, K.K.R., Leung, H.: A deep and scalable unsupervised machine learning system for cyber-attack detection in large-scale smart grids. IEEE Access 7, 80778–80788 (2019)

    Google Scholar 

  25. Niu, X., Tomsovic, K.: Dynamic detection of false data injection attack in smart grid using deep learning. In: 2019 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference (ISGT), pp. 1–6. IEEE (2019)

    Google Scholar 

  26. Habibi, M.R., Blaabjerg, F., et al.: Detection of false data injection cyber-attacks in DC microgrids based on recurrent neural networks. IEEE J. Emerg. Sel. Top. Power Electron. (2020)

    Google Scholar 

  27. Wang, C., Tindemans, S., Palensky, P.: Detection of false data injection attacks using the autoencoder approach. arXiv preprint arXiv:2003.02229 (2020)

  28. Haque, N.I., Shahriar, Md.H., Dastgir, Md.G.: Machine learning in generation, detection, and mitigation of cyberattacks in smart grid: a survey. arXiv:2010.00661v1 [cs.CR] (2020)

  29. Haque, N.I., et al.: Machine learning in generation, detection, and mitigation of cyberattacks in smart grid: a survey. arXiv preprint arXiv:2010.00661 (2020)

  30. Tufail, S., Parvez, I., Batool, S., Sarwat, A.: Detection, and Mitigation Techniques for the Smart Grid, Energies, A Survey on Cybersecurity Challenges (2021)

    Google Scholar 

  31. Guidelines for Smart Grid Cybersecurity, National Institute of Standards and Technology (2014)

    Google Scholar 

  32. Analysis of the Cyber Attack on the Ukrainian Power Grid, Electricity Information Sharing and Analysis Center (2016)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Texas Tech University, Lubbock, USA

    Pallavi Zambare & Ying Liu

Authors
  1. Pallavi Zambare
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  2. Ying Liu
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Corresponding authors

Correspondence to Pallavi Zambare or Ying Liu .

Editor information

Editors and Affiliations

  1. Khalifa University, Abu Dhabi, United Arab Emirates

    Deepak Puthal

  2. University of North Texas, Denton, TX, USA

    Saraju Mohanty

  3. University of Missouri at Kansas City, Kansas City, MO, USA

    Baek-Young Choi

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Zambare, P., Liu, Y. (2024). Understanding Cybersecurity Challenges and Detection Algorithms for False Data Injection Attacks in Smart Grids. In: Puthal, D., Mohanty, S., Choi, BY. (eds) Internet of Things. Advances in Information and Communication Technology. IFIPIoT 2023. IFIP Advances in Information and Communication Technology, vol 684. Springer, Cham. https://doi.org/10.1007/978-3-031-45882-8_22

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  • DOI: https://doi.org/10.1007/978-3-031-45882-8_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-45881-1

  • Online ISBN: 978-3-031-45882-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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