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Method for the Adaptive Neutralization of Structural Breaches in Cyber-Physical Systems Based on Graph Artificial Neural Networks

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Abstract

This paper presents a model of threats in cyber-physical systems (CPSs) with examples of attacks and potential negative consequences for systems for various purposes. It is concluded that the critical consequences of attacks are associated with data exchange breaches within a system. Therefore, the CPS security problem is confined to restoring the data exchange efficiency. To neutralize the consequences, which are negative for data exchange, it is proposed to use graph artificial neural networks (ANNs). The contemporary architectures of graph ANNs are reviewed. An algorithm for the generation of a synthetic training dataset is developed and implemented to model the network traffic intensity and load of devices in a system based on graph centrality measures. A graph ANN is trained for the problem of reconfiguring the graph of a CPS.

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Funding

This study was supported by the Ministry of Science and Higher Education of the Russian Federation as a part of the World-Class Research Center Program “Advanced Digital Technologies” (contract no. 075-15-2022-311 dated from April 20, 2022).

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Authors and Affiliations

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

    E. B. Aleksandrova & A. A. Shtyrkina

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  1. E. B. Aleksandrova
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  2. A. A. Shtyrkina
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Correspondence to A. A. Shtyrkina.

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The authors of this work declare that they have no conflicts of interest.

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Translated by E. Glushachenkova

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Aleksandrova, E.B., Shtyrkina, A.A. Method for the Adaptive Neutralization of Structural Breaches in Cyber-Physical Systems Based on Graph Artificial Neural Networks. Aut. Control Comp. Sci. 57, 1076–1083 (2023). https://doi.org/10.3103/S0146411623080011

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