Abstract
Artificial neural network (ANN) provides superior accuracy for nonlinear alternating current (AC) state estimation (SE) in smart grid over traditional methods. However, research has discovered that ANN could be easily fooled by adversarial examples. In this paper, we initiate a new study of adversarial false data injection (FDI) attack against AC SE with ANN: by injecting a deliberate attack vector into measurements, the attacker can degrade the accuracy of ANN SE while remaining undetected. We propose a population-based algorithm and a gradient-based algorithm to generate attack vectors. The performance of these algorithms are evaluated through simulations on IEEE 9-bus, 14-bus and 30-bus systems under various attack scenarios. Simulation results show that DE is more effective than SLSQP on all simulation cases. The attack examples generated by DE algorithm successfully degrade the ANN SE accuracy with high probability.
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Acknowledgement
The work of T. Shu is supported in part by NSF under grants CNS-1837034, CNS-1745254, CNS-1659965, and CNS-1460897. Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the author(s) and do not necessarily reflect the views of NSF.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Liu, T., Shu, T. (2019). Adversarial False Data Injection Attack Against Nonlinear AC State Estimation with ANN in Smart Grid. In: Chen, S., Choo, KK., Fu, X., Lou, W., Mohaisen, A. (eds) Security and Privacy in Communication Networks. SecureComm 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 305. Springer, Cham. https://doi.org/10.1007/978-3-030-37231-6_21
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