Neuro-symbolic models for ensuring cybersecurity in critical cyber-physical systems

This paper presents the results of a comprehensive study on the application of the neuro-symbolic approach for detecting and preventing cyber threats in railway systems, a critical component of cyber-physical infrastructures. The increasing complexity and integration of physical systems with digital technologies have made such infrastructures vulnerable to cyberattacks, where breaches can result in severe consequences, including system failures, financial losses, and threats to public safety and the environment. The objective of this study was to assess the effectiveness of the neuro-symbolic approach, which combines artificial neural networks with symbolic algorithms, in detecting and mitigating cyber threats in dynamic environments. The methodology involved simulating various cyberattack scenarios on a test architecture for railway system security, followed by applying the neuro-symbolic model for threat detection and response. Results showed that the neuro-symbolic approach demonstrated high accuracy in detecting cyber threats and was particularly effective in adapting to new and unknown types of attacks. Compared to traditional methods, this approach significantly improved dete