This study introduces a sensor-centric cybersecurity framework for railway infrastructure that extends Failure Mode and Effects Analysis (FMEA) from traditional reliability evaluation into the domain of cyber-induced failures affecting data integrity, availability and authenticity. The contribution lies in bridging regulatory obligations of the NIS2 Directive with field-layer monitoring by enabling risk indicators to evolve dynamically rather than remain static documentation artefacts. The approach is demonstrated using a scenario-based dataset collected from approximately 250 trackside, rolling-stock, environmental and power-monitoring sensors deployed over a 25 km operational segment, with representative anomalies generated through controlled spoofing, replay and injection conditions. Risk was evaluated using RPN scores derived from Severity–Occurrence–Detectability scales, while anomaly-detection performance was observed through detection-latency variation, changes in RPN distribution, and qualitative responsiveness of timestamp-based alerts. Instead of presenting a fixed benchmark, the results show how evidence from real sensor streams can recalibrate O and D factors in near-real-time and reduce undetected exposure windows, enabling measurable compliance documentation aligned with NIS2 Article 21. The findings confirm that coupling FMEA with streaming telemetry creates a verifiable risk-evaluation loop and supports a transition toward continuous, evidence-driven cybersecurity governance in railway systems.

This study presents the results of research aimed at developing and verifying a computational algorithm that allows maximizing the mass transported by a train on a given railway line. The algorithm evaluates traction capability based on various models of train motion resistance described in the literature. Seven motion resistance models were implemented and compared to experimental data collected from a real train run on a selected non-electrified railway line. This comparison enabled the adjustment of model parameters to better reflect real traction conditions. The proposed method allows railway operators to estimate the maximum permissible train mass for specific line characteristics, ensuring operational safety and energy efficiency. The analysis confirmed that the choice of motion resistance model significantly affects the estimated traction performance. The optimized model (CNTK (2)) showed the best agreement with empirical results, demonstrating its applicability for operational analyses. The developed algorithm can be adapted to any location within the railway network, provided that an appropriate motion resistance model is applied. Ultimately, the method may support the development of digital tools for optimizing freight train parameters, improving cost efficiency, and enhancing the sustainability of rail transport.