Enhancing Cloud Security Through Ranking of Multi-Layer Authentication Based on a Complex q-Rung Orthopair Fuzzy Frank CRITIC-MABAC Model

Abstract

Multi-layer authentication (MLA) plays a critical role in enhancing cloud security by preventing unauthorized access and mitigating evolving cyber threats. Despite the availability of numerous MLA strategies, their evaluation and selection remain challenging due to conflicting criteria, uncertain information, and subjective expert judgments. Existing decision-making approaches often have limited capability to model complex uncertainty and effectively capture the interrelationships among evaluation criteria, which may affect the reliability of the final ranking results. Motivated by these limitations, this study proposes a novel Complex q-Rung Orthopair Fuzzy Frank CRITIC-MABAC (Cq-ROFF-CRITIC-MABAC) framework for the evaluation and ranking of MLA strategies in cloud computing environments. The proposed framework integrates Cq-ROFSs to represent uncertain information, Frank aggregation operators to combine expert assessments, the CRITIC method to determine objective criteria weights, and the MABAC method to rank competing alternatives. A real-world case study is conducted to demonstrate the applicability and effectiveness of the proposed framework. The results indicate that the proposed method generates stable, consistent, and discriminative rankings, while comparative and sensitivity analyses confirm its robustness and superiority over existing MCDM approaches. The proposed framework contributes a comprehensive decision-support model for cloud security assessment and provides practical guidance for cloud service providers and security practitioners in selecting the most appropriate MLA strategy.