A Triangular Fractional Fuzzy CRITIC–COPRAS Framework for Cybersecurity Initiative Prioritisation

Cybersecurity organizations often need to determine the implementation priority of multiple complementary security initiatives under limited resources. This study develops a multi-criteria group decision-making (MCGDM) framework to support the prioritisation of cybersecurity initiatives rather than selecting a single mutually exclusive technique. To capture the uncertainty and imprecision inherent in expert evaluations, Triangular Fractional Fuzzy Numbers (Tri-FFNs), an extension of Fractional Fuzzy Numbers (FFNs), are introduced. First, new operational laws based on Frank t-norms are developed, and a Triangular Fractional Fuzzy Frank Weighted Average (Tri-FFFWA) aggregation operator is proposed to aggregate expert assessments. The weights of decision-makers are determined using the correlation consensus degree. Subsequently, an integrated CRITIC–COPRAS model is developed, in which the CRITIC method objectively determines the criteria weights based on their contrast intensity and inter-criteria correlation, while COPRAS is employed to establish the priority of cybersecurity initiatives. The proposed framework is applied to a real-world cybersecurity scenario involving complementary initiatives, including multi-factor authentication, encryption, patch management, zero-trust security, and AI-powered threat detection, to identify their relative implementation priorities under resource constraints. Comparative and sensitivity analyses are further conducted to validate the robustness and effectiveness of the proposed approach. The results demonstrate that the framework provides a systematic, transparent, and flexible mechanism for supporting cybersecurity implementation prioritisation under uncertain expert information.