A Novel Malware Classification Method Based on Memory Image Representation

Abstract

Malware classification methods based on memory image representation have received increasing attention. However, the characteristics of the memory management mechanism and efficiency of the classification model are not well considered in previous works, which hinders the classifier from extracting high-quality features and consequently results in poor performance. Motivated by this, we propose a novel malware classification method. First, we add an Efficient Convolutional Block Attention Module (E-CBAM) to select important features with fewer parameters and less computational cost. Then, we integrate our attention module into a pre-trained EfficientNet-B0 to extract features efficiently. Moreover, data augmentation and label smoothing are adopted to mitigate model overfitting. Finally, extensive experiments on a realistic dataset testify to the performance and superiority of our method in both known and unknown malware classification.