Selection of Cloud Security by Employing MABAC Technique in the Environment of Hesitant Bipolar Complex Fuzzy Information

• Published in: IEEE access 2024, Vol.12, p.123127-123148
• Main Authors: Waqas, Hafiz Muhammad, Emam, Walid, Mahmood, Tahir, Rehman, Ubaid Ur, Yin, Shi
• Format: Article
• Language: English
• Subjects: aggregation operators; Cloud computing security; Cloud security; Data breach; Fuzzy sets; hesitant bipolar complex fuzzy set; Linguistics; MABAC technique; MAGDM; Regulation; Security
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3436687

The term "cloud security (CS)" describes the collection of procedures and tools intended to defend networks, data, apps, and systems used in cloud computing from possible security risks and unauthorized access. Data breaches, identity and access management, network security, adherence to industry and governmental standards, and the security of third-party services and apps are a few of the major issues with CS. Selecting the best CS becomes critical for resolving all these problems. Within the context of hesitant bipolar complex fuzzy sets (HBCFSs) theory, we address in this study optimal selection utilizing various conceptions of aggregation operators (AOs). The notion of HBCFSs gives us a valuable framework by providing the hesitancy nature of any object along with its positive and negative aspects. Moreover, HBCFSs are a valuable tool to eliminate the vagueness and uncertainty of any given information. In this manuscript, by utilizing the framework of HBCFSs we developed some new AOs which are obliging to convert the set of information into a singleton value. Then by utilizing these AOs we calculate and aggregate all the numerical significance of CS. To handle our supposed problem of CS the mainly developed AOs are hesitant bipolar complex fuzzy (HBCF) weighted averaging (HBCFWA), HBCF ordered weighted averaging (HBCFOWA), HBCF weighted geometric (HBCFWG), HBCF ordered weighted geometric (HBCFOWG), generalized HBCF weighted averaging (GHBCFWA), generalized HBCF weighted geometric (GHBCFWG) operators. Furthermore, we develop the multi-attributive border approximation area comparison (MABAC) method to address our multi-attribute group decision-making (MAGDM) problem of CS. Moreover, in this manuscript, we propose and analyze a CS-related numerical case study to identify the optimal CS. Lastly; to demonstrate the advantages and superiority of the interpretive work, we compared our suggested methodology with other extant ideas.