<![CDATA[The exponential growth of internet-connected devices, particularly accelerated by the COVID-19 pandemic, has brought forth a critical global challenge: safeguarding the security of transmitted information. The integrity and functionality of these devices face significant threats from various forms of malware, leading to behavioral distortions. Consequently, a vital aspect of cybersecurity entails accurately identifying and classifying such malware, enabling the implementation of appropriate countermeasures. Existing literature has explored diverse approaches for malware identification, encompassing static and dynamic analysis techniques like signature-based, behavior-based, and heuristic-based methods. However, these approaches face a key issue of inadequately identifying unknown malware variants, often resulting in misclassifications of new strains as benign. To tackle this challenge, this study introduces a novel ensemble-based approach for identifying and classifying malware on Windows platforms, with a specific focus on detecting new and previously unknown variants. The proposed approach leverages multiple machine learning schemes to identify elusive unknown malware that proves challenging for existing methods. ]]>
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