Functional stability assessment and adaptation for critical infrastructure facilities

Ensuring functional stability of critical infrastructure facilities (CIFs) under conditions of uncertainty and dynamic threats remains a critical challenge. Existing approaches insufficiently integrate technical, cybersecurity, and human-related factors.

This study proposes an information-cognitive approach based on a hybrid model combining Bayesian Trust Networks and fuzzy logic. The model incorporates expert knowledge and evaluates the mutual influence of information security, cybersecurity, human factors, and vulnerability indicators. The Mamdani algorithm is used for probabilistic estimation under uncertainty.

Numerical experiments conducted in the GeNIe environment demonstrate that the proposed model effectively supports decision-making. Scenario analysis shows that adjusting key cybersecurity and vulnerability factors increases the probability of achieving sufficient functional stability above the critical threshold.

The proposed hybrid framework improves interpretability and adaptability of functional stability assessment. It enables flexible reasoning under uncertainty and supports real-time decision-making for critical infrastructure management. The approach can be applied across different categories of CIFs and extended with additional data-driven components.