Interval Valued Neutrosophic Set with Machine Learning Model Dynamic Malware Detection in Digital Security

Traditional signature-based detection techniques are useless against new forms of malware due to their fast development, which poses a serious cybersecurity risk. People, businesses, and governments are all affected by this expanding threat, highlighting the urgent need for robust malware detection systems. Due to their reliance on predetermined signatures, traditional machine learning-based techniques frequently fail to identify threats that have not yet been identified and instead rely on static and dynamic malware analysis. To improve malware detection performance across a variety of datasets, this study assesses traditional ML. Interval Valued Neutrosophic Set (IVNS) is used in this study to overcome vague information. The Neutrosophic Model is used to evaluate and rank six ML models. The results show support vector machine is the best ML Model for dynamic malware detection in digital security.