BAYESIAN APPROACH TO TRAINING DEEP NEURAL NETWORKS FOR RISK MANAGEMENT OF CYBER-PHYSICAL SYSTEMS
DOI:
https://doi.org/10.28925/2663-4023.2025.28.785Keywords:
cyber-physical systems; Bayesian approach; neural networks; deep learning models; risk managementAbstract
This article is devoted to the study of the use of the Bayesian approach in training deep neural networks for risk management tasks in cyber-physical systems. Cyber-physical systems (CPS) are complex complexes that combine computational and physical components and require a clear approach to security and risk management. Traditional threat and vulnerability assessment methods often do not take into account the high level of uncertainty inherent in complex environments with the dynamic nature of cyber threats. Instead, the Bayesian approach provides probabilistic modelling and allows for the integration of a priori knowledge of the system into the training of neural networks.
This paper provides a detailed overview of the theoretical foundations of Bayesian Neural Networks (BNNs), compares them with classical deterministic deep learning models, and provides examples of their application in adaptive risk assessment.
The article analyses existing methods of variational Bayesian inference and their role in improving the accuracy and reliability of potential threats forecasting. The main stages of the research methodology, including the design of the neural network architecture.
The results provide experimental evaluations and comparisons with classical approaches, as well as demonstrate practical aspects of implementing Bayesian BNNs for real-world CFAs. The discussion highlights the advantages and disadvantages of the Bayesian approach, emphasises the need to take into account uncertainties and suggests directions for further research. Particular attention is paid to the use of variable quality of sensor data and mechanisms of system response to dynamic conditions in the context of risk assessment.
These conclusions and practical recommendations can serve as a basis for implementing the described methods in various applications, such as industrial automation, intelligent transport systems, critical infrastructure, etc.
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