TRANSFER LEARNING AND SMOTE ALGORITHM FOR IMAGE-BASED MALWARE CLASSIFICATION
Prima Bouchaib, Mohammed Bouhorma · 2021
In recent years, the volume and type of malware is growing, which increases the need of improving a detection and classification malware systems. Nowadays, deep convolutional neural networks (CNNs) have recently proven to be very successful for malware classification due to their performance on images classification. However, their effectiveness is degraded with the unbalanced malware families. In this paper, we propose a malware classification framework using CNN-based deep learning architecture, including a SMOTE technique "Synthetic Minority Oversampling Technique" to balance the dataset (malwares families).