Assessing cybersecurity resilience of digital ports using a BN-FAIR framework

The digitalization of seaports enhances efficiency but heightens exposure to complex cyber–physical disruptions threatening transport continuity and supply chains. This study proposes a probabilistic framework to quantify and strengthen port cyber–physical resilience by integrating Bayesian Networks (BN) for causal inference with the Factor Analysis of Information Risk (FAIR) model for financial impact estimation. The model captures interdependencies across six domains: cyber layer, physical layer, interconnection layer, organizational, external threat, and individual factors. Sensitivity analysis identifies critical vulnerabilities such as legacy software, unpatched systems, hardware failures, and limited cybersecurity awareness. Six targeted strategies, including Zero-Trust Architecture, predictive maintenance, and adaptive governance, are mapped to high-impact nodes to guide investment priorities. By combining probabilistic reasoning with economic quantification, the BN–FAIR framework provides transport policymakers and port operators with a transparent, data-driven tool to enhance resilience and sustainability in maritime logistics systems.