Functional stability assessment and adaptation for critical infrastructure facilities.

Area of Science:

• Engineering
• Computer Science
• Risk Management

Background:

• Critical infrastructure facilities (CIFs) face significant challenges in maintaining functional stability due to uncertainty and dynamic threats.
• Current assessment methods often fail to adequately integrate technical, cybersecurity, and human-related factors.

Purpose of the Study:

• To propose an information-cognitive approach for assessing the functional stability of CIFs.
• To develop a hybrid model that combines Bayesian Trust Networks and fuzzy logic for comprehensive risk evaluation.

Main Methods:

• A hybrid model integrating Bayesian Trust Networks and fuzzy logic was developed.
• Expert knowledge was incorporated to evaluate the interplay of information security, cybersecurity, human factors, and vulnerability indicators.
• The Mamdani algorithm facilitated probabilistic estimation under conditions of uncertainty.

Main Results:

• Numerical experiments demonstrated the model's effectiveness in supporting decision-making for CIF functional stability.
• Scenario analysis indicated that optimizing cybersecurity and vulnerability factors significantly increases the probability of achieving critical stability thresholds.
• The hybrid framework enhanced interpretability and adaptability in functional stability assessments.

Conclusions:

• The proposed hybrid framework offers flexible reasoning under uncertainty for real-time CIF management.
• The approach is applicable across various CIF categories and can be extended with data-driven components.
• This study provides a robust method for improving the resilience and security of critical infrastructure.