ThreatVisionAI: A Hybrid CNN-ViT Framework for Image-Based Malware Classification
Traditional malware detection methods struggle to generalize to obfuscated or previously unseen threats. This paper introduces ThreatVisionAI, a hybrid malware family classification framework that integrates a raw-image CNN, a wavelet-based CNN, and a Vision Transformer (ViT) to capture complementary spatial, frequency-domain, and global relational features in malware images. The wavelet-based CNN captures multi-scale frequency information that helps distinguish closely related families, while the ViT branch models long-range dependencies across the image. Evaluated on the Malimg dataset, ThreatVisionAI achieves 98.01% accuracy and a weighted F1 score of 0.9742, with wavelet-domain featur...
| Authors | Allyson Taylor, Prashanth BusiReddyGari |
|---|---|
| Primary category | cs.CR |
| Published | 4 July 2026 |
| Last updated at source | 4 July 2026 |
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