The rapid evolution of malware obfuscation and packing techniques significantly undermines the effectiveness of traditional static detection approaches. Transforming malware binaries into grayscale or RGB images enables learning-based classification, yet existing CNN- and ViT-based models depend heavily on fixed-resolution inputs and exhibit poor robustness under cross-resolution distortions. This study proposes a lightweight and sample-adaptive Multi-Scale Vision Transformer (MSA-ViT) for efficient and robust malware image classification. MSA-ViT leverages a fixed set of input scales and integrates them using a Scale-Attention Fusion (SAF) module, where the largest-scale CLS token serves as the query to dynamically aggregate cross-scale representations. To mitigate scale bias and improve generalization, SimCLR self-supervised pre-training and KL-divergence-based cross-scale consistency regularization are incorporated. Experiments on the Malimg and MaleVis datasets demonstrate that MSA-ViT achieves accuracies of 98.5% and 96.0%, respectively, outperforming existing baselines. Robustness evaluations further show that performance degradation remains below 1.8% under scaling, padding, and FGSM perturbations. Attention-based visualizations confirm the interpretability of the fusion mechanism. Overall, MSA-ViT provides an accurate, robust, and computationally efficient solution for image-based malware classification.
Yang et al. (Thu,) studied this question.