Mitigating the Impacts of Covert Cyber Attacks in Smart Grids Via Reconstruction of Measurement Data Utilizing Deep Denoising Autoencoders

Saeed Ahmed, YoungDoo Lee, Seung-Ho Hyun and Insoo Koo
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Saeed Ahmed: School of Electrical Engineering, University of Ulsan, Ulsan 44610, Korea
YoungDoo Lee: School of Electrical Engineering, University of Ulsan, Ulsan 44610, Korea
Seung-Ho Hyun: School of Electrical Engineering, University of Ulsan, Ulsan 44610, Korea
Insoo Koo: School of Electrical Engineering, University of Ulsan, Ulsan 44610, Korea

Energies, 2019, vol. 12, issue 16, 1-24

Abstract: As one of the most diversified cyber-physical systems, the smart grid has become more decumbent to cyber vulnerabilities. An intelligently crafted, covert, data-integrity assault can insert biased values into the measurements collected by a sensor network, to elude the bad data detector in the state estimator, resulting in fallacious control decisions. Thus, such an attack can compromise the secure and reliable operations of smart grids, leading to power network disruptions, economic loss, or a combination of both. To this end, in this paper, we propose a novel idea for the reconstruction of sensor-collected measurement data from power networks, by removing the impacts of the covert data-integrity attack. The proposed reconstruction scheme is based on a latterly developed, unsupervised learning algorithm called a denoising autoencoder, which learns about the robust nonlinear representations from the data to root out the bias added into the sensor measurements by a smart attacker. For a robust, multivariate reconstruction of the attacked measurements from multiple sensors, the denoising autoencoder is used. The proposed scheme was evaluated utilizing standard IEEE 14-bus, 39-bus, 57-bus, and 118-bus systems. Simulation results confirm that the proposed scheme can handle labeled and non-labeled historical measurement data and results in a reasonably good reconstruction of the measurements affected by attacks.

Keywords: autoencoder; cyber-security; cyber-assaults; deep learning; self-healing smart grids; state estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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