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Malware Detection System using Machine Learning & Deep Learning Technique
(International Islamic University Chittagong, 2023-07) Karim, Miskatul; Hossain, Md Jubair
These days, with the amount and variety of malware increasing at an exponential rate, it is imperative to use innovative methods for quickly and precisely detecting this dangerous software. The fast velocity at which malware spreads makes manual heuristic inspections of malware analyses inefficient for both discovering new malware and keeping up with it. Machine-learning techniques have therefore become increasingly popular. By automating the examination of static and dynamic studies, these methods can classify unknown malware according to how close it is to recognized families and combine malware that exhibits similar behavior. While data mining and machine learning approaches have been applied in the past, this paper demonstrates how deep learning networks can improve accuracy even further. By building neural networks that have more potentially varied.