A new semantic validation approach using unpackers as executable contracts reveals widespread bugs in packer ID tools; repairs raise coverage by up to 58.6% and improve downstream malware classification by over 13.6%.
Malware images: visualization and automatic classification
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.CR 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
ViPER uses a LoRA-adapted ViT-B/14 with dual heads for malware classification and packing detection plus a gating mechanism and weighted losses to reach 0.8521 balanced accuracy on 200k Windows PE images while detecting packing at 0.9949 AUC.
Pretraining plus Mixup/TrivialAugment and a feature pyramid network lift macro-F1 from 0.65 to 0.69 on 43-class malware image classification while cutting training epochs from 96 to 10.
citing papers explorer
-
Semantic Validation of Packer Identification Tools: Characterization, Repair, and Downstream Impact
A new semantic validation approach using unpackers as executable contracts reveals widespread bugs in packer ID tools; repairs raise coverage by up to 58.6% and improve downstream malware classification by over 13.6%.
-
ViPER: Vision-based Packing-Aware Encoder for Robust Malware Detection
ViPER uses a LoRA-adapted ViT-B/14 with dual heads for malware classification and packing detection plus a gating mechanism and weighted losses to reach 0.8521 balanced accuracy on 200k Windows PE images while detecting packing at 0.9949 AUC.
-
Image-Based Malware Type Classification on MalNet-Image Tiny: Effects of Multi-Scale Fusion, Transfer Learning, Data Augmentation, and Schedule-Free Optimization
Pretraining plus Mixup/TrivialAugment and a feature pyramid network lift macro-F1 from 0.65 to 0.69 on 43-class malware image classification while cutting training epochs from 96 to 10.