Boyan Cui1,2, Huijuan Wang1,*, Yongjun Qi1,*, Hongce Chen1, Quanbo Yuan1,3, Dongran Liu1, Xuehua Zhou1
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5531-5553, 2025, DOI:10.32604/cmc.2025.070101
- 23 October 2025
Abstract The proliferation of malware and the emergence of adversarial samples pose severe threats to global cybersecurity, demanding robust detection mechanisms. Traditional malware detection methods suffer from limited feature extraction capabilities, while existing Vision Transformer (ViT)-based approaches face high computational complexity due to global self-attention, hindering their efficiency in handling large-scale image data. To address these issues, this paper proposes a novel hybrid enhanced Vision Transformer architecture, HERL-ViT, tailored for malware detection. The detection framework involves five phases: malware image visualization, image segmentation with patch embedding, regional-local attention-based feature extraction, enhanced feature transformation, and classification. Methodologically,… More >