OPTIMIZING REAL-TIME BIG DATA SECURITY: A COMPLEX Q-RUNG ORTHOPAIR FUZZY BWM-MARCOS TECHNIQUE FOR ADVANCED CYBER DEFENSE SOLUTIONS

Abstract

The rapid expansion of real-time big data systems has intensified cybersecurity challenges, making the selection of effective cyber defense solutions a crucial and complex decision-making task. Owing to the presence of uncertainty, vagueness, and hesitation in expert evaluations, conventional assessment approaches may fail to provide reliable decision support. To address this challenge, this study presents a complex q-rung orthopair fuzzy best-worst method and measurement alternatives and ranking according to the compromise solution (Cq-ROF BWM-MARCOS) framework for evaluating advanced cyber defense solutions in real-time big data security environments. The proposed framework utilizes Cq-ROFSs to represent uncertain expert judgments more comprehensively through membership and non-membership information. Subsequently, the best-worst method is employed to determine the relative importance of evaluation criteria, while the MARCOS method is used to assess the utility of alternatives and establish their final ranking with respect to ideal and anti-ideal reference solutions. To demonstrate the applicability of the proposed framework, a real-world case study involving advanced cyber defense solutions is conducted. The obtained results identify the most suitable security alternative and reveal the effectiveness of the proposed approach in handling complex and uncertain cybersecurity decision environments. The proposed framework provides a systematic and reliable decision-support tool for organizations and security practitioners seeking to strengthen real-time big data protection through informed selection of cyber defense technologies.