Adaptive Multi-Criteria Modeling for Maritime Cyber Risk Management and Resilience Evaluation

This paper introduces an integrated mathematical model for managing cyber risks in maritime transport systems. The model combines the Analytic Hierarchy Process (AHP), Failure Mode and Effects Analysis (FMEA), Bayesian probability updating, reliability assessment methods, and decision support system (DSS) optimization mechanisms. Its goal is to quantitatively assess and reduce risks from cyberattacks targeting primary autonomous surface vessels and port-based energy infrastructures equipped with energy storage systems and soft open point technologies. Using a multi-criteria evaluation framework, the research performs comprehensive risk quantification, dynamically updates the probabilities of cyber incidents, and examines the technical and operational impacts of cyber interference on system performance. Three operational scenarios are analyzed: normal operation, cyberattack, and adaptive mitigation, to show how risk levels vary and how decision support algorithms can effectively restore system stability. Results indicate that cyber threats may significantly increase overall risk levels, while adaptive optimization within the decision support framework can reduce these risks and improve system resilience. This methodology provides a scientific basis for developing adaptive cyber-resilience strategies for maritime transport and supports the future use of digital twins for vessels and port infrastructures as part of Smart Maritime Infrastructure. The modeling showed that the integration of AHP–FMEA–BN allows reducing the total risk index by 23% for autonomous vessels and by 18% for port power systems compared to the baseline scenario. The scientific novelty of the study lies in the development of an adaptive model that combines multi-criteria assessment, failure analysis, and Bayesian probability updating for dynamic assessment of cyber risks in the maritime sector.