AI and the World's Hard Problems · Theoretical computer science · Nature (computational biology) · 2026-10-16
Multi-level malware subfamily attribution from RGB executable images using attention-augmented deep learning
Malware analysis requires decisions at several levels, from detection to family and fine-grained subfamily attribution. The authors propose a hierarchical deep learning framework that takes RGB images of Windows PE files and performs detection, family classification and 33-subfamily attribution in one multi-output architecture. Four attention-augmented transfer learning models reached 99% binary, 96–97% family-level and 91–95% subfamily-level accuracy, with macro F1-scores up to 0.89 for fine-grained attribution.