Abstract
A major concern in smart power grids is when malicious or manipulated data is injected into measurement data due to malicious activities. Several approaches have been investigated to counter such false data injection attacks (FDIAs). However, such data-driven detectors present two major limitations. First, they neglect capturing the grid’s spatial characteristics. Second, they offer limited attack identification to familiar types of FDIAs since they are present within the model’s train sets. To conquer such limitations, we propose the use of an artificial intelligence-based graph autoencoder (GAE) for FDIAs detection. Our proposed detector offers three main advantages compared to existing detectors. First, it employs the operation of graph convolution to apprehend the grid’s spatial characteristics. Second, it offers an unsupervised autoencoder-based anomaly detection that requires only benign samples under normal operation for training. Third, it outperforms existing detectors by 16–47% in FDIAs detection rate (DR) when tested against unseen FDIAs on an IEEE 39-bus system.
This work was supported by NSF EPCN Awards 2220346 and 2220347.
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References
An, D., et al.: Data integrity attack in dynamic state estimation of smart grid: Attack model and countermeasures. IEEE Trans. Autom. Sci. Eng. 19(3), 1631–1644 (2022). Jul
Zhang, Z., et al.: Cyber-physical coordinated risk mitigation in smart grids based on attack-defense game. IEEE Trans. Power Syst. 37(1), 530–542 (2022). Jan
Huang, K., et al.: False data injection attacks detection in smart grid: A structural sparse matrix separation method. IEEE Trans. Netw. Sci. Eng. 8(3), 2545–2558 (2021). Jul.
Boyaci, O., et al.: Graph neural networks based detection of stealth false data injection attacks in smart grids. IEEE Syst. J. 16(2), 2946–2957 (2022). Jun
Esmalifalak, M., et al.: Detecting stealthy false data injection using machine learning in smart grid. IEEE Syst. J. 11(3), 1644–1652 (2017). Sept
Lu, X., et al.: False data injection attack location detection based on classification method in smart grid. In: International Conference on Artificial Intelligence and Advanced Manufacture (AIAM), pp. 133–136. Manchester, United Kingdom (2020)
Wang, D., et al.: Detection of power grid disturbances and cyber-attacks based on machine learning. J. Inf. Secur. Appl. 46, 42–52 (2019). Jun.
Musleh, A.S., et al.: A survey on the detection algorithms for false data injection attacks in smart grids. IEEE Trans. Smart Grid 11(3), 2218–2234 (2020). May
Takiddin, A., et al.: Robust electricity theft detection against data poisoning attacks in smart grids. IEEE Trans. Smart Grid 12(3), 2675–2684 (2021). May
Xue, D., et al.: Detection of false data injection attacks in smart grid utilizing ELM-Based OCON framework. IEEE Access 7, 31 762–31 773 (2019)
Wang, S., et al.: 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). Sept
Wang, Y., et al.: Kfrnn: An effective false data injection attack detection in smart grid based on kalman filter and recurrent neural network. IEEE Internet Things J. 9(9), 6893–6904 (2022). May
Takiddin, A., Ismail, M., Serpedin, E.: Robust data-driven detection of electricity theft adversarial evasion attacks in smart grids. IEEE Trans. Smart Grid 14(1), 663–676 (2023). Jan.
Drayer, E., et al.: Detection of false data injection attacks in power systems with graph fourier transform. In: IEEE Global Conference on Signal and Information Processing, pp. 135–140. Anaheim, CA, USA (2018)
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). Jun
Ramakrishna, R., et al.: Detection of false data injection attack using graph signal processing for the power grid. In: IEEE Global Conference on Signal and Information Processing (GSIP). Ottawa, ON, Canada (2019)
Takiddin, A., et al.: Detection of electricity theft false data injection attacks in smart grids. In: 30th European Signal Processing Conference (EUSIPCO)), pp. 1541–1545. Belgrade, Serbia (2022)
Zimmerman, R.D., et al.: Matpower: Steady-state operations, planning, and analysis tools for power systems research and education. IEEE Trans. Power Syst. 26(1), 12–19 (2011). Feb
Takiddin, A., et al.: A graph neural network multi-task learning-based approach for detection and localization of cyberattacks in smart grids. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023), pp. 1–5. Rhodes Island, Greece (2023)
Hasnat, M., et al.: Detection and locating cyber and physical stresses in smart grids using graph signal processing (2020). arXiv:2006.06095
Stamile, C., et al.: Graph Machine Learning: Take Graph Data to the Next Level by Applying Machine Learning Techniques and Algorithms. Packt Publishing, Birmingham, United Kingdom (2021)
Takiddin, A., et al.: Deep autoencoder-based anomaly detection of electricity theft cyberattacks in smart grids. IEEE Syst. J. 16(3), 4106–4117 (2022). Sept
Wu, L., et al.: Graph Neural Networks: Foundations, Frontiers, and Applications. Springer, Singapore (2022)
Takiddin, A., et al.: Generalized graph neural network-based detection of false data injection attacks in smart grids. IEEE Trans. Emerg. Top. Comput. Intell. 7(3), 618–630 (2023). Jun.
Krishna, V., et al.: ARIMA-Based modeling and validation of consumption readings in power grids. In: Critical Information Infrastructures Security, pp. 199–210. Springer Intl. Publishing
Takiddin, A., et al.: Data-driven detection of stealth cyber-attacks in dc microgrids. IEEE Syst. J. 16(4), 6097–6106 (2022). Dec
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Takiddin, A., Ismail, M., Atat, R., Davis, K.R., Serpedin, E. (2024). Graph Autoencoder-Based Detection of Unseen False Data Injection Attacks in Smart Grids. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2023. Lecture Notes in Networks and Systems, vol 822. Springer, Cham. https://doi.org/10.1007/978-3-031-47721-8_16
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