| Autor: |
Igor Kotenko, Igor Saenko, Oleg Lauta, Mikhail Karpov |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Energies, Vol 14, Iss 18, p 5963 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1996-1073 |
| DOI: |
10.3390/en14185963 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
|
Nepřihlášeným uživatelům se plný text nezobrazuje |
K zobrazení výsledku je třeba se přihlásit.
|
