Android Malware Detection Technology Based on SC-ViT and Multi-Feature Fusion

With the continuous breakthroughs in deep learning within the field of computer vision, an increasing number of researchers have begun exploring image-based Android malware detection. For example, recent years have seen widespread application of Vision Transformer (ViT) models in Android malware detection, which have demonstrated significant effectiveness. However, ViT-based Android malware detection methods still face challenges, such as the loss of local features and insufficient capture of edge information and texture features, which limit their detection capabilities. To address these challenges, this paper proposes an Android malware detection method based on multi-feature fusion RGB images and the SC-ViT model. The SC-ViT model consists of two branches: the SC (Swin-Transformer CBAM) branch is responsible for extracting global information and capturing long-range dependencies, while the SPP (Spatial Pyramid Pooling Net, SPP-net) branch focuses on extracting multi-scale spatial features. These features are subsequently fused using an IAFF(Iterative Attentional Feature Fusion) module. Experimental results show that the proposed model achieves a detection accuracy of 99.50% and a detection precision of 99.23%, significantly surpassing the 96.82% accuracy achieved by the baseline ViT model, demonstrating its superiority and innovation in Android malware detection tasks.