International Journal of Neutrosophic Science IJNS 2690-6805 2692-6148 10.54216/IJNS https://www.americaspg.com/journals/show/3112 2020 2020 Department of Software Engineering, College of Engineering, University of Business and Technology, Jeddah, Saudi Arabia Louai Louai A neutrosophic set (NS) is an advanced computational technique that accesses uncertain information via three membership functions. A soft expert set (SES) is derived from the hypothesis of a “soft set” with computer technology. Currently, this method is utilized in various domains such as intelligent systems, measurement theory, probability theory, cybernetics, game theory, and so on. Internet user faces a myriad of risks with the development of malware worldwide. The most prominent type of malware, Ransomware, encrypts confidential data without releasing the files until the user makes a ransom payment. Internet of Things (IoT) framework is a wide region of Internet-related devices with further computation capacities with storage capabilities that can be damaged by malware creators. Ransomware is a cruel and new malware on Internet with increasing attack levels. Ransomware encrypts the whole information to make users incapable of accessing important information and their files. In this article, we propose a Complex Proportional Assessment Based Neutrosophic Approach for Ransomware Detection in Cybersecurity (CPABNA-RDCS) methodology in IoT environment. The objective of the CPABNA-RDCS approach is to identify and categorize the ransomware to accomplish cybersecurity in the IoT network. Primarily, the CPABNA-RDCS method exploits min-max normalization for scaling the input dataset into relevant format. Meanwhile, the ransomware classification takes place via Complex Proportional Assessment Based Neutrosophic (CPABN) method. Finally, grey wolf optimizer (GWO) is employed for optimum hyperparameter choice of the CPABN system. The experimental results of the CPABNA-RDCS method are inspected on benchmark data. The simulation analysis emphasized the developments of the CPABNA-RDCS method over other existing techniques. 2025 2025 22 32 10.54216/IJNS.250203 https://www.americaspg.com/articleinfo/21/show/3112