Methodology for Management of the Protection System of Smart Power Supply Networks in the Context of Cyberattacks

Igor Kotenko, Igor Saenko, Oleg Lauta and Mikhail Karpov
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Igor Kotenko: Laboratory of Computer Security Problems, St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 39, 14th Liniya, 199178 St. Petersburg, Russia
Igor Saenko: Laboratory of Computer Security Problems, St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 39, 14th Liniya, 199178 St. Petersburg, Russia
Oleg Lauta: Department of Integrated Information Security, Admiral Makarov State University of Maritime and Inland Shipping, 5/7 Dvinskaya St., 198035 St. Petersburg, Russia
Mikhail Karpov: Department of Information and Telecommunication Security, Saint-Petersburg Signal Academy, 3 Tikhoretsky Av., 194064 St. Petersburg, Russia

Energies, 2021, vol. 14, issue 18, 1-39

Abstract: This paper examines an approach that allows one to build an efficient system for protecting the information resources of smart power supply networks from cyberattacks based on the use of graph models and artificial neural networks. The possibility of a joint application of graphs, describing the features for the functioning of the protection system of smart power supply networks, and artificial neural in order to predict and detect cyberattacks is considered. The novelty of the obtained results lies in the fact that, on the basis of experimental studies, a methodology for managing the protection system of smart power supply networks in conditions of cyberattacks is substantiated. It is based on the specification of the protection system by using flat graphs and implementing a neural network with long short-term memory, which makes it possible to predict with a high degree of accuracy and fairly quickly the impact of cyberattacks. The issues of software implementation of the proposed approach are considered. The experimental results obtained using the generated dataset confirm the efficiency of the developed methodology. It is shown that the proposed methodology demonstrates up to a 30% gain in time for detecting cyberattacks in comparison with known solutions. As a result, the survivability of the Self-monitoring, Analysis and Reporting technology (SMART) grid (SG) fragment under consideration increased from 0.62 to 0.95.

Keywords: power supply; protection system; graph theory; SMART grid system; data transmission network; cyberattack; control methodology; LSTM neural network (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: 2021
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