Year: 2025
Type: article
Abstract: . Railway communication networks based on Ethernet and Wi-Fi are increasingly becoming targets of cyber threats that can disrupt data exchange, control systems operation, and information security. The growing volume of transmitted data and the integration of intelligent control systems raise new requirements for cybersecurity, prompting the need for advanced approaches to threat detection and mitigation. This study aimed to enhance the cybersecurity of railway communication systems through the integration of algebraic modelling and machine learning techniques, including neural networks and a neuro-symbolic approach. The research included a vulnerability assessment of railway networks and the development of mathematical models for optimising rolling stock routing, infrastructure management, and cyber threat detection. Algorithms for identifying anomalies in railway network traffic based on autoencoders are proposed, enabling the detection of data flow deviations in real time. Experimental modelling was conducted using a dataset that included real and simulated traffic associated with cyberattacks. The results demonstrated a 35% reduction in network load, a 22% improvement in threat blocking efficiency, and an anomaly detection accuracy of 82.3%. In addition, over 87% of potentially malicious requests were automatically blocked without operator intervention. The system achieved a false positive rate of 6.2% and a false negative rate of 5.1%, confirming the effectiveness of combining neural networks with symbolic rule sets. The proposed methods also enabled traffic route optimisation and network load balancing. The practical significance of the study lies in the development of adaptive cybersecurity mechanisms for railway communication systems that enhance resilience against emerging threats, including protocol-level attacks. The integration of artificial intelligence methods with algebraic modelling improves the accuracy of cyber threat prediction, enables traffic routing optimisation, and supports the creation of adaptive incident response strategies
Author: Serhii Yevdokymov
Institution: Kherson State University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414985166