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Identification of Cyber Threats in Networks of Industrial Internet of Things Based on Neural Network Methods Using Memory

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Abstract

It is proposed to use modern artificial neural networks to identify cyber threats in networks of the Industrial Internet of Things. The modeling of an industrial system under the influence of cyberattacks was carried out. As a result of the experiments, the optimal configuration parameters of the recurrent LSTM network with a confirmed number of layers and states have been determined.

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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. Identification of Cyber Threats in Networks of Industrial Internet of Things Based on Neural Network Methods Using Memory. Aut. Control Comp. Sci. 54, 900–906 (2020). https://doi.org/10.3103/S0146411620080180

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