Divakarla, U.Reddy, K.H.K.Chandrasekaran, K.2026-02-062022Procedia Computer Science, 2022, Vol.215, , p. 148-15718770509https://doi.org/10.1016/j.procs.2022.12.017https://idr.nitk.ac.in/handle/123456789/29750Now-a-day's malicious software is increasing in numbers and at present becomes more harmful for any digital equipment like mobile, tablet, and computers. Traditional techniques like static and dynamic analysis, signature-based detection methods are become absolute and not effective at all. The advanced techniques like code encryption and code packing techniques can be used to hide detection; polymorphic malware is a new class of malware that changes their code structure from time to time to avoid detection, so there is a need for an intelligent system which can efficiently analyze the features of a new, unknown executable file and classify it correctly. There have been learning-based malware detection systems proposed in the literature, but most of those proposed approaches present a high accuracy over a small dataset, whereas the performance is very poor over industry-standard datasets. Operating system like windows is always in prime malware target because of the sheer high number of users. This paper proposes a simple, deep learning-based detection approachthat classifies a specified executable into benign or harmful. It has been trained using EMBER, an industry-level Windows malware dataset and tests with an accuracy of 87.76%. © 2023 The Authors. Published by Elsevier B.V.Deep LearningMalwareNeural NetworksOperating System securityStatic AnalysisWindows