Abstract
The smart grid is a critical cyber-physical infrastructure; attackers may exploit vulnerabilities to launch cyber attacks. The smart grid control system relies heavily on the communication infrastructure among sensors, actuators, and control systems, making it vulnerable to cyber-attacks. We propose a method for injecting a false data injection attack (FDIA) into the smart grid using generative adversarial networks (GAN). A sample of disturbance vectors generated using deep temporal convolutional GAN (DTCGAN) is superimposed on the original phasor measurement unit (PMU) measurements to generate compromised data. The performance results show a significant impact of the developed attack on data-driven methods for grid monitoring. Specifically, we demonstrated the attack on a transient stability application.
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Acknowledgements
We are thankful to the Ministry of Education (Govt. of India) and CPRI-funded project: EE/PB/CPRI/2022/8.85 for supporting the work.
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Dash, S.P., Khandeparkar, K.V. (2023). A False Data Injection Attack on Data-Driven Strategies in Smart Grid Using GAN. In: Fujita, H., Wang, Y., Xiao, Y., Moonis, A. (eds) Advances and Trends in Artificial Intelligence. Theory and Applications. IEA/AIE 2023. Lecture Notes in Computer Science(), vol 13926. Springer, Cham. https://doi.org/10.1007/978-3-031-36822-6_27
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