This study develops a decision framework to evaluate and prioritize cybersecurity measures for smart grids, rather than introducing a new machine learning or intrusion-detection algorithm. The framework combines three multi-criteria decision-making techniques—Fuzzy Best–Worst Method (FBWM), Fuzzy Measurement Alternatives and Ranking according to Compromise Solution (FMARCOS), and the Heronian function—to translate expert judgment under uncertainty into a clear set of priorities. Data were obtained through structured interviews with specialists in cybersecurity and smart grid engineering. The evaluation considered six criteria: network security, intrusion detection systems, artificial intelligence (AI)-driven anomaly detection, blockchain for data integrity, system resilience, and multi-layered defense strategies. FBWM was applied to estimate the relative importance of the criteria, and FMARCOS was used to rank cybersecurity strategy alternatives based on their weighted performance. The Heronian function was included to reflect interactions among criteria and to avoid treating them as fully independent. The results place system resilience and AI-driven anomaly detection at the top of the priority set, indicating that smart grid protection depends heavily on adaptive monitoring and strong recovery capability. Sensitivity analysis shows that the top-ranked alternative remains stable across different weighting scenarios. Overall, the study offers a transparent and reproducible prioritization approach that can support policymakers and infrastructure managers when allocating cybersecurity investments in smart grid environments.
Mizrak et al. (Tue,) studied this question.