With the increasing diversity and concealment of malicious code, traditional detection methods often face high costs and instability when dealing with unknown malware. This study aims to propose a lightweight and efficient malware detection model to meet the application requirements in resource-constrained environments. This paper proposes a lightweight malware detection model based on SecureViT. The model achieves efficient feature extraction and accurate classification by introducing the ACF module and MSDC module. The ACF module enhances the model′s ability to model global context information, while the MSDC module further improves the richness of feature representation through multi-scale feature extraction and dynamic significance adjustment. Experimental results show that the SecureViT model achieves classification accuracies of 97.46%, 91.17%, and 95.49% on the Malimg, Virus-MNIST, and BIG2015 datasets, respectively, with a computational cost of only 1.71 GMAC, significantly improving detection performance and effectively reducing computational costs. This model demonstrates excellent detection accuracy and low computational complexity, making it highly applicable in resource-constrained environments.