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Increasing the Sensitivity of the Method of Early Detection of Cyber-Attacks in Telecommunication Networks Based on Traffic Analysis by Extreme Filtering

Author

Listed:
  • Andrey Privalov

    (Emperor Alexander I Saint-Petersburg State Transport University, 9 Moskovsky pr., St. Petersburg 190031, Russia)

  • Vera Lukicheva

    (Emperor Alexander I Saint-Petersburg State Transport University, 9 Moskovsky pr., St. Petersburg 190031, Russia)

  • Igor Kotenko

    (Saint-Petersburg Institute for Informatics and Automation of Russian Academy of Sciences (SPIIRAS), 39, 14 Liniya, St. Petersburg 199178, Russia)

  • Igor Saenko

    (Saint-Petersburg Institute for Informatics and Automation of Russian Academy of Sciences (SPIIRAS), 39, 14 Liniya, St. Petersburg 199178, Russia)

Abstract

The paper proposes a method for improving the accuracy of early detection of cyber attacks with a small impact, in which the mathematical expectation is a fraction of the total, and the pulse repetition period is quite long. Early detection of attacks against telecommunication networks is based on traffic analysis using extreme filtering. The algorithm of fuzzy logic for deciding on the results of extreme filtering is suggested. The results of an experimental evaluation of the proposed method are presented. They demonstrate that the method is sensitive even with minor effects. In order to eliminate the redundancy of the analyzed parameters, it is enough to use the standard deviation and the correlation interval for decision making.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2774-:d:365702
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    References listed on IDEAS

    as
    1. Rong Zhang & Baabak Ashuri & Yong Deng, 2017. "A novel method for forecasting time series based on fuzzy logic and visibility graph," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(4), pages 759-783, December.
    2. 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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    Cited by:

    1. 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.
    2. 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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