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Generative Adversarial Network for Detecting Cyber Threats in Industrial Systems

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Proceedings of International Scientific Conference on Telecommunications, Computing and Control

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 220))

Abstract

The transition from the information economy to the digital presents new challenges to the community related to the development of breakthrough technologies, a network of cyber-physical systems, artificial intelligence, and big data. When creating digital platforms, a number of difficulties arise: the large dimension of the digital infrastructure and its heterogeneity, poorly established information interaction between the segments, the lack of a common approach to ensuring cybersecurity, and high dependence on personnel qualification and reliability of equipment. The introduction of the digital economy leads to an increase in the risk of cyber threats associated with problems of access control between systems, regulation of information, and control flows. In this paper, for solving cyber threat detection tasks, it is proposed to use generative adversarial neural networks. The paper presents training and testing algorithms of the neural network. The result of the experiments demonstrated high accuracy at cyber threat detection.

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Acknowledgements

The work was funded by the Russian Federation Presidential grants for support of young scientists and postgraduate students (SP-443.2019.5).

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Authors and Affiliations

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

    Vasiliy Krundyshev & Maxim Kalinin

Authors
  1. Vasiliy Krundyshev
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  2. Maxim Kalinin
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Corresponding author

Correspondence to Vasiliy Krundyshev .

Editor information

Editors and Affiliations

  1. Higher School of Software Engineering, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia

    Nikita Voinov

  2. Department of Computer Science and Biomedical Engineering, Graz University of Technology, Graz, Austria

    Tobias Schreck

  3. School of Maths, Computer Science and Engineering, City, University of London, London, UK

    Sanowar Khan

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Krundyshev, V., Kalinin, M. (2021). Generative Adversarial Network for Detecting Cyber Threats in Industrial Systems. In: Voinov, N., Schreck, T., Khan, S. (eds) Proceedings of International Scientific Conference on Telecommunications, Computing and Control. Smart Innovation, Systems and Technologies, vol 220. Springer, Singapore. https://doi.org/10.1007/978-981-33-6632-9_1

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  • DOI: https://doi.org/10.1007/978-981-33-6632-9_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-33-6631-2

  • Online ISBN: 978-981-33-6632-9

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