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Ensuring Cybersecurity of Digital Production Using Modern Neural Network Methods

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Abstract

The transition from the information economy to the digital one presents new challenges for society associated with the development of disruptive technologies, a network of cyber-physical systems, artificial intelligence and big data. When creating digital platforms, a number of difficulties arise: the large size of the digital infrastructure and its heterogeneity, poorly established information interaction between segments, the lack of a unified approach to ensuring cybersecurity and a high dependence on the qualifications of personnel and equipment reliability. The introduction of the digital economy leads to an increase in the risk of cyber threats associated with access control problems between information flow regulation and control systems. To solve the problems of detecting cyber threats, it is proposed to use generative adversarial neural networks. Algorithms for learning and testing a neural network were presented. The results of the experiments have demonstrated that the proposed solution is highly accurate in detecting cyberattacks.

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Funding

The work was funded by the Russian Federation Presidential grants for support of leading scientific schools (SP-443.2019.5).

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Authors and Affiliations

  1. Peter the Great St. Petersburg Polytechnic University, 195251, St. Petersburg, Russia

    V. M. Krundyshev

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  1. V. M. Krundyshev
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Correspondence to V. M. Krundyshev.

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The authors declare that they have no conflicts of interest.

Additional information

Translated by S. Avodkova

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Krundyshev, V.M. Ensuring Cybersecurity of Digital Production Using Modern Neural Network Methods. Aut. Control Comp. Sci. 54, 786–792 (2020). https://doi.org/10.3103/S0146411620080179

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