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Automatic Control and Computer Sciences

Erschienen in:

01.12.2019

Forecasting the State of Components of Smart Grids for Early Detection of Cyberattacks

verfasst von: D. S. Lavrova

Erschienen in: Automatic Control and Computer Sciences | Ausgabe 8/2019

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Abstract

The author proposes an approach for predicting the state of Smart Grid components, which is based on a combination of the mathematical techniques of the Kalman filter and machine learning. Prediction of the state will make it possible to detect cyberattacks implemented against a Smart Grid at an early stage.

Vorheriger Artikel Detection of Information Security Breaches in a Digital Production System Based on the Component Interaction Model

Nächster Artikel Maintaining Cyber Sustainability in Industrial Systems Based on the Concept of Molecular-Genetic Control Systems

Locke, G. and Gallagher, P.D., NIST Framework and Roadmap for Smart Grid Interoperability Standards, Release 1.0, National Institute of Standards and Technology, 2010, p. 33.

Pavlenko, E. and Zegzhda, D., Sustainability of cyber-physical systems in the context of targeted destructive influences, 2018 IEEE Industrial Cyber-Physical Systems(ICPS), 2018, pp. 830–834. https://doi.org/10.1109/ICPHYS.2018.8390814

Federal Energy Regulatory Commission Assessment of Demand Response & Advanced Metering, 2018.

Kalman, R.E., A new approach to linear filtering and prediction problems, J. Basic Eng., 1960, vol. 82, no. 1, pp. 35–45.MathSciNetCrossRef

Loebis, D., et al., Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system, Control Eng. Pract., 2004, vol. 12, no. 12, pp. 1531–1539.CrossRef

Dataset 1: Power System Datasets. https://sites.google.com/a/uah.edu/tommy-morris-uah/ics-data-sets. Accessed July 2, 2019.

Titel: Forecasting the State of Components of Smart Grids for Early Detection of Cyberattacks
verfasst von: D. S. Lavrova
Publikationsdatum: 01.12.2019
Verlag: Pleiades Publishing
Erschienen in: Automatic Control and Computer Sciences / Ausgabe 8/2019
Print ISSN: 0146-4116
Elektronische ISSN: 1558-108X
DOI: https://doi.org/10.3103/S0146411619080133

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