aspg-21-2775-1791468692 20261008141132 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Neutrosophic Science IJNS 2692-6148 2690-6805 2024 24 2 Alsadig Ahmed Applied Management Program, Applied College at Muhyle, King Khalid University, Saudi Arabia Cybersecurity has become a primary concern as the financial sectors generally handle increasing cyber-attacks and an increasing danger of financial crime. Recently, ransomware attacks have intensified, affecting enterprises, and crucial infrastructure worldwide. Ransomware employs sophisticated encryption techniques to encrypt data on the targeted device, then requests payment for decrypting the data. Artificial intelligence (AI) approaches involving ML were progressively employed in the domain of cybersecurity and significantly subsidized to preventing and detecting variety of threats. On the other hand, the several researchers that employed ML to identify ransomware are still constrained by the accuracy of models, the complication of malware, the high false-positive rate, and the lack of setting up the appropriate analysis environment. Therefore, there is a need to design efficient ransomware detection based on ML algorithms. This work introduces a modified Single Value Neutrosophic Fuzzy Soft Expert Set (M-SVNFSES) technique for cyberattack detection. The main purpose of the M-SVNFSES system is to detect and recognize the existence of cyberattacks in the financial sectors. In the M-SVNFSES technique, min-max normalization is used as an initial pre-processing stage. For the identification of cyberattacks in the financial sectors, the M-SVNFSES technique uses the SVNFSES model. To enhance its performance, the M-SVNFSES technique makes use of a bat optimization algorithm (BOA). The performance of the M-SVNFSES methodology was extensively studied using financial datasets. The experimental outcomes depicted that the M-SVNFSES method reaches optimal detection performance in attack detection process 2024 246 257 2775 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJNS.240222 https://www.americaspg.com/journal/21/article/2775 https://www.americaspg.com/storage/01715243368.pdf