Abstract
Memory analysis is essential for spotting malicious programs since it can record a variety of traits and behaviors. The detection rate and sophisticated malware obfuscation are two major challenges in malware detection, even though there has been a lot of research in the area. An effective framework that focuses on identifying obfuscation and hidden malware is desperately needed because advanced malware employs obfuscation and other tactics to avoid detection. The VolMemLyzer has been improved in this study to focus on hidden and obfuscated malware when used with a stacked ensemble machine learning model to build a framework for effectively identifying malware. It is one of the most updated memory feature extractors for learning systems.
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