Efficientnet-Based Deep Learning for Malware Classification: A Dynamic Distribution Adaptation Approach

Abstract

Malware is a general name given to all malicious software that threatens and prevents the use of information systems. Computers, which have become mandatory in daily life, are constantly under the threat of malware as well as facilitating human life. Therefore, the detection of malware that threatens computer systems is important. This study focuses on the classification of malware. In the study, a deep learning model based on the EfficientNet architecture and the Dynamic Distribution Adaptation Network approach were proposed and these proposed models were tested using the Microsoft Malware Classification Challenge (MMCC) and Dumpware10 datasets. In the study, the process of converting malware into images was discussed and the EfficientNet model was used as the basis for the classification of these images. The EfficientNet backbone-based Dynamic Distribution Adaptation Network achieved 97% accuracy in the MMCC dataset and 96% accuracy in the Dumpware10 dataset. As a result, the EfficientNet architecture proved the effectiveness of deep learning in the classification of malware and cybersecurity.

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