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This presentation focuses on the development of MalwareClassifier, an adaptive machine learning-based framework for malware detection. Addressing the challenges posed by evolving obfuscation techniques and limitations of traditional static approaches, the objective is to create a scalable, GPU-accelerated system capable of accurate two-phase classification. The framework leverages dynamic features from the CIC-MalMem-2022 dataset, incorporating data preparation, feature extraction, and...