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

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...

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Veröffentlicht in:Energies (Basel) Jg. 12; H. 16; S. 3091
Hauptverfasser: Ahmed, Saeed, Lee, YoungDoo, Hyun, Seung-Ho, Koo, Insoo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 11.08.2019
Schlagworte:
Assaults
autoencoder
Bias
Corruption
cyber-assaults
cyber-security
Decision trees
deep learning
Electric power
Electricity distribution
Energy consumption
Fault diagnosis
International conferences
Intrusion detection systems
Investigations
Machine learning
Principal components analysis
self-healing smart grids
Sensors
state estimation
Trends
United States > US
ISSN:1996-1073, 1996-1073
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1996-1073
1996-1073
DOI:10.3390/en12163091