On the security of ANN-based AC state estimation in smart grid

With the deployment of new elements in the smart grid, traditional state estimation methods are challenged by growing dynamics and system size. Artificial neural network (ANN) based AC state estimation has been shown to provide faster results than traditional methods. However, researchers have disco...

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Vydané v:Computers & security Ročník 105; s. 102265
Hlavní autori: Liu, Tian, Shu, Tao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Amsterdam Elsevier Ltd 01.06.2021
Elsevier Sequoia S.A
Predmet:
AC State estimation
Accuracy
Adversarial learning
Algorithms
Artificial neural networks
Bus transport
Computer simulation
Cyber security
Cybersecurity
Deployment
Estimation
Evolutionary algorithms
Evolutionary computation
False data
False data injection attack
Measuring instruments
Neural networks
Quadratic programming
Robustness
Simulation
Smart grid
State estimation
False data injection attack
AC State estimation
Adversarial learning
Smart grid
Cyber security
ISSN:0167-4048, 1872-6208
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Shrnutí:With the deployment of new elements in the smart grid, traditional state estimation methods are challenged by growing dynamics and system size. Artificial neural network (ANN) based AC state estimation has been shown to provide faster results than traditional methods. However, researchers have discovered that ANNs could be easily fooled by adversarial examples. In this paper, we initiate a new study of adversarial false data injection attacks against ANN-based state estimation. By injecting a deliberate attack vector into measurements, the attacker can degrade the accuracy of ANN state estimation while remaining undetected. We propose two algorithms to generate the attack vectors, a population-based algorithm (differential evolution or DE) and a gradient-based algorithm (sequential least square quadratic programming or SLSQP). The performance of these algorithms is 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 the DE algorithm successfully degrade the ANN state estimation accuracy with high probability (more than 80% in all simulation cases), despite having a small number of compromised meters and low injection strength. We further discuss the potential defense strategy to mitigate such attacks, which provides insights for robustness improvement in future research.
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ISSN:0167-4048
1872-6208
DOI:10.1016/j.cose.2021.102265