Advanced Malware Detection Methods for Polymorphic Virus Identification

Polymorphic viruses pose a significant challenge to traditional malware detection methods due to their ability to modify their code structure with each infection, effectively evading signature-based detection. This paper explores advanced malware detection methods tailored for identifying polymorphic viruses, focusing on the integration of machine learning, deep learning and hybrid approaches. Through a comprehensive analysis of static and dynamic features extracted from executables and URLs, this study employs classifiers such as Random Forests and Support Vector Machines (SVMs) to enhance detection accuracy. The SVM classifier demonstrated superior performance, achieving a detection accuracy of 96.23% compared to the 93.67% accuracy of the Random Forest classifier. This research highlights the potential of combining static and dynamic analysis with machine learning techniques to address the limitations of traditional methods, offering a robust framework for detecting and mitigating polymorphic and zero-day threats.