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An Approach to Detecting Cyber Attacks against Smart Power Grids Based on the Analysis of Network Traffic Self-Similarity

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Listed:
  • Igor Kotenko

    (St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS), 39, 14 Liniya, 199178 St. Petersburg, Russia)

  • Igor Saenko

    (St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS), 39, 14 Liniya, 199178 St. Petersburg, Russia)

  • Oleg Lauta

    (Admiral Makarov State University of Maritime and Inland Shipping, 5/7 Dvinskaya st., 198035 St. Petersburg, Russia)

  • Aleksander Kribel

    (Saint-Petersburg Signal Academy, 3 Tikhoretsky av., 194064 St. Petersburg, Russia)

Abstract

The paper discusses an approach for detecting cyber attacks against smart power supply networks, based on identifying anomalies in network traffic by assessing its self-similarity property. Methods for identifying long-term dependence in fractal Brownian motion and real network traffic of smart grid systems are considered. It is shown that the traffic of a telecommunication network is a self-similar structure, and its behavior is close to fractal Brownian motion. Fractal analysis and mathematical statistics are used as tools in the development of this approach. The issues of a software implementation of the proposed approach and the formation of a dataset containing network packets of smart grid systems are considered. The experimental results obtained using the generated dataset have demonstrated the existence of self-similarity in the network traffic of smart grid systems and confirmed the fair efficiency of the proposed approach. The proposed approach can be used to quickly detect the presence of anomalies in the traffic with the aim of further using other methods of cyber attack detection.

Suggested Citation

  • Igor Kotenko & Igor Saenko & Oleg Lauta & Aleksander Kribel, 2020. "An Approach to Detecting Cyber Attacks against Smart Power Grids Based on the Analysis of Network Traffic Self-Similarity," Energies, MDPI, vol. 13(19), pages 1-24, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:19:p:5031-:d:418721
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    References listed on IDEAS

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    1. Andrey Privalov & Vera Lukicheva & Igor Kotenko & Igor Saenko, 2020. "Increasing the Sensitivity of the Method of Early Detection of Cyber-Attacks in Telecommunication Networks Based on Traffic Analysis by Extreme Filtering," Energies, MDPI, vol. 13(11), pages 1-18, June.
    2. Steffen Unkel & C. Paddy Farrington & Paul H. Garthwaite & Chris Robertson & Nick Andrews, 2012. "Statistical methods for the prospective detection of infectious disease outbreaks: a review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(1), pages 49-82, January.
    3. Torriti, Jacopo, 2012. "Demand Side Management for the European Supergrid: Occupancy variances of European single-person households," Energy Policy, Elsevier, vol. 44(C), pages 199-206.
    4. Andrey Privalov & Vera Lukicheva & Igor Kotenko & Igor Saenko, 2019. "Method of Early Detection of Cyber-Attacks on Telecommunication Networks Based on Traffic Analysis by Extreme Filtering," Energies, MDPI, vol. 12(24), pages 1-14, December.
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    Citations

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    Cited by:

    1. Taha Selim Ustun, 2022. "Cybersecurity in Smart Grids," Energies, MDPI, vol. 15(15), pages 1-3, July.
    2. Igor Kotenko & Igor Saenko & Oleg Lauta & Mikhail Karpov, 2021. "Methodology for Management of the Protection System of Smart Power Supply Networks in the Context of Cyberattacks," Energies, MDPI, vol. 14(18), pages 1-39, September.
    3. Andrey Privalov & Igor Kotenko & Igor Saenko & Natalya Evglevskaya & Daniil Titov, 2021. "Evaluating the Functioning Quality of Data Transmission Networks in the Context of Cyberattacks," Energies, MDPI, vol. 14(16), pages 1-19, August.

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