Malware Through the Lens of Computer Vision

Abstract

Image-based malware classification through binary-to-image encodings has become a popular, quick-to-run and disassembly-free alternative to signature-based methods. Near-perfect accuracies are often reported, alongside weakly substantiated claims of resilience to malware obfuscation by adversaries. We present the first controlled, head-to-head comparison of four such encodings - grayscale and RGB byteplots, Markov bigram plots, and sliding-window Shannon entropy - under a fixed ResNet-18 classifier and a balanced dataset spanning seven packer conditions. To test whether high accuracy reflects genuine family structure or exploitable shortcuts, we pair the comparison with a diagnostic framework combining random- forest feature baselines, TLSH similarity analysis, HiResCAM and occlusion-based explainability methods. No single diagnostic suffices; only their combination separates shortcut-driven scores from genuine learning. We find that encoding performance cannot be judged by accuracy alone; models may fit to shortcuts such as file-size and near-duplicate samples, rather than learning malware structure. Entropy is found to be the dominant and most obfuscation-resilient encoding, while exhibiting the most evidence of genuine family-discriminative learning.