Multi-modal malware classification with hierarchical consistency and saliency-constrained adversarial training

Summary

The paper proposes a multi-modal malware classification approach that combines hierarchical consistency with saliency-constrained adversarial training. It is motivated by the growing volume of malware, with over 1.2 billion samples by 2024 and more than 450,000 new detections per day, and by the weaknesses of signature-based, behavior-based and heuristic detection, including sandbox evasion and high false positives. The excerpt covers only the introduction and does not report the method's results.