Ensuring Cybersecurity of Digital Production Using Modern Neural Network Methods


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.

This is a preview of subscription content, access via your institution.

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Fig. 1.
Fig. 2.


  1. 1

    Schwab, K., The Fourth Industrial Revolution, Penguin UK, 2017.

    Google Scholar 

  2. 2

    Klau, T., As the boards of directors of companies decide on the introduction of advanced technologies, Joint Stock Co.: Corp. Governance Issues, 2017, vol. 1, no. 2, pp. 30–31.

    Google Scholar 

  3. 3

    Belov, V.B., A new paradigm for industrial development in Germany – the Industry 4.0 strategy, Sovrem. Evr., 2016, no. 5, pp. 11–22.

  4. 4

    Zhou, L., Yeh, K., Hancke, G., Liu, Z., and Su, C., Security and privacy for the industrial Internet of Things: An overview of approaches to safeguarding endpoints, IEEE Signal Process. Mag., 2018, vol. 35, no. 5, pp. 76–87.

    Article  Google Scholar 

  5. 5

    Global, M.K., The Internet of Things: Mapping the Value Beyond the Hype, New York: McKinsey & Company, 2015.

    Google Scholar 

  6. 6

    Huawei Global Industry Vision, Unfolding the Industry Blueprint of an Intelligent World, 2018.

  7. 7

    Kaspersky Lab, Industry 4.0, 2019.

  8. 8

    Ovasapyan, T., Moskvin, D., and Kalinin, M., Using neural networks to detect internal intruders in vanets, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 954–958.

    Article  Google Scholar 

  9. 9

    Lavrova, D., Zegzhda, D., and Yarmak, A., Using GRU neural network for cyber-attack detection in automated process control systems, IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Sochi, 2019, pp. 1–3.

  10. 10

    Zegzhda, P., Zegzhda, D., Pavlenko, E., and Ignatev, G., Applying deep learning techniques for Android malware detection, ACM International Conference Proceeding Series, 2018. https://doi.org/10.1145/3264437.3264476

  11. 11

    Kalinin, M.O., Zubkov, E.A., Suprun, A.F., and Pechenkin, A.I., Prevention of attacks on dynamic routing in self-organizing adhoc networks using swarm intelligence, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 977–983.

    Article  Google Scholar 

  12. 12

    Stepanova, T., Kalinin, M., Baranov, P., and Zegzhda, D., Homogeneity analysis of power consumption for information security purposes, Proceedings of the 3rd International Conference of Security of Information and Networks, 2010, pp. 113–117.

  13. 13

    Kalinin, M.O., Lavrova, D.S., and Yarmak, A.V., Detection of threats in cyberphysical systems based on deep learning methods using multidimensional time series, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 912–917.

    Article  Google Scholar 

  14. 14

    Lavrova, D., Poltavtseva, M., and Shtyrkina, A., Security analysis of cyber-physical systems network infrastructure, 2018 IEEE Industrial Cyber-Physical Systems, ICPS 2018, 2018, pp. 818–823.

    Google Scholar 

  15. 15

    Zegzhda, D., Lavrova, D., and Poltavtseva, M., Multifractal security analysis of cyberphysical systems, Nonlinear Phenom. Complex Syst. (Dordrecht, Neth.), 2019, vol. 22, no. 2, pp. 196–204.

  16. 16

    Lavrova, D., Pechenkin, A., and Gluhov, V., Applying correlation analysis methods to control flow violation detection in the Internet of Things, Autom. Control Comput. Sci., 2015, vol. 49, no. 8, pp. 735–740.

    Article  Google Scholar 

  17. 17

    Zegzhda, P., Zegzhda, D., Kalinin, M., Pechenkin, A., Minin, A., and Lavrova, D., Safe integration of siem systems with Internet of Things: Data aggregation, integrity control, and bioinspired safe routing, ACM International Conference Proceeding Series, 2016, pp. 81–87. https://doi.org/10.1145/2947626.2947639

  18. 18

    With QuickType, Apple wants to do more than guess your next text. It wants to give you an AI. https://www.wired.com/2016/06/apple-bringing-ai-revolution-iphone/. Accessed September 31, 2019.

  19. 19

    Xiong, W., et al., The Microsoft 2017 Conversational Speech Recognition System: Technical Report. https://www.microsoft.com/en-us/research/publication/microsoft-2017-conversational-speech-recognition-system/. Accessed September 31, 2019.

  20. 20

    Belenko, V., Chernenko, V., Kalinin, M., and Krundyshev, V., Evaluation of GAN applicability for intrusion detection in self-organizing networks of cyber physical systems, 2018 International Russian Automation Conference, Conference Proceedings, 2018. https://doi.org/10.1109/RUSAUTOCON.2018.8501783

  21. 21

    Kalinin, M., Demidov, R., and Zegzhda, P., Hybrid neural network model for protection of dynamic cyber infrastructure, Nonlinear Phenom. Complex Syst. (Dordrecht, Neth.), 2019, vol. 22, no. 4, pp. 375–382.

  22. 22

    Demidov, R.A., Zegzhda, P.D., and Kalinin, M.O., Threat analysis of cyber security in wireless adhoc networks using hybrid neural network model, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 971–976.

    Article  Google Scholar 

  23. 23

    Cui, Y. and Wang, W., Colorless video rendering system via generative adversarial networks, IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Dalian, 2019, IEEE, 2019, pp. 464–467.

  24. 24

    Goodfellow, I., et al., Generative adversarial nets, Advances in Neural Information Processing Systems, 2014, pp. 2672–2680.

  25. 25

    Zhu, J.Y., et al., Unpaired image-to-image translation using cycle-consistent adversarial networks, Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 2223–2232.

  26. 26

    Schlegl, T., et al., Unsupervised anomaly detection with generative adversarial networks to guide marker discovery, International Conference on Information Processing in Medical Imaging, Boone, 2017; Lect. Notes Comput. Sci., 2017, pp. 146–157.

  27. 27

    Anderson, H., Woodbridge, J., and Filar, B., DeepDGA: Adversarially-tuned domain generation and detection, Proceedings of the 2016 ACM Workshop on Artificial Intelligence and Security, Vienna, 2016, 2016, pp. 13–21.

  28. 28

    Tensorflow. https://www.tensorflow.org/. Accessed September 31, 2019.

  29. 29

    Keras. https://keras.io/. Accessed September 31, 2019.

  30. 30

    Hodo, E., et al., Threat analysis of IoT networks using artificial neural network intrusion detection system, 2016 International Symposium on Networks, Computers and Communications (ISNCC), Yasmine Hammamet, 2016, pp. 1–6.

  31. 31

    Itano, F., de Abreu de Sousa, M., and Del-Moral-Hernandez, E., Extending MLP ANN hyper-parameters optimization by using genetic algorithm, International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, 2018, pp. 1–8.

  32. 32

    Ingre, B. and Yadav, A., Performance analysis of NSL-KDD dataset using ANN, International Conference on Signal Processing and Communication Engineering Systems, Guntur, 2015, pp. 92–96.

  33. 33

    Beigh, B. and Peer, V., Performance evaluation of different intrusion detection system: An empirical approach, International Conference on Computer Communication and Informatics, Coimbatore, 2014, pp. 1–7.

  34. 34

    Network Simulator NS-3. https://www.nsnam.org/. Accessed September 31, 2019.

  35. 35

    Kalinin, M.O., Zubkov, E.A., Suprun, A.F., and Pechenkin, A.I., Prevention of attacks on dynamic routing in self-organizing adhoc networks using swarm intelligence, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 977–983.

    Article  Google Scholar 

  36. 36

    Fawcett, T., An introduction to ROC analysis, Pattern Recognit. Lett., 2006, vol. 27, no. 8, pp. 861–874.

    Article  Google Scholar 

Download references


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

Author information



Corresponding author

Correspondence to V. M. Krundyshev.

Ethics declarations

The authors declare that they have no conflicts of interest.

Additional information

Translated by S. Avodkova

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation


  • generative adversarial networks
  • artificial intelligence
  • cybersecurity
  • neural networks
  • digital manufacturing
  • GAN
  • IIoT