Malware Image Classification: A Literature Survey

Abstract: Malware classification is an important cybersecurity task because identifying malware families can help analysts understand threats and support appropriate response. This literature survey reviews sixteen studies addressing malware classification and detection through image-based learning, one-dimensional signal representations, feature selection, memory-dump analysis, multimodal fusion, hybrid deep learning, and Android-focused methods. Particular attention is given to binary-to-image conversion and convolutional neural networks (CNNs), which learn visual patterns from executable bytes without requiring conventional disassembly. The reviewed studies report promising results, including accuracy above 90% in several image-based settings and 99.05% accuracy for an epoch-selection ensemble on the Malimg dataset. However, reported results are not directly comparable because studies use different datasets, class definitions, representations, and evaluation protocols. Recurring challenges include dataset bias, generalization to unseen malware, class imbalance, information loss during representation and resizing, model complexity, and the trade-off between static analysis and execution-dependent evidence. The survey identifies the need for careful evaluation of binary-to-image preprocessing, lightweight CNN architectures, reproducible comparisons, and validation across datasets. These findings provide a basis for the project’s static, image-based malware-family classification direction while leaving final model configuration to empirical evaluation.

Keywords: Malware Classification, Binary-to-Image Conversion, Convolutional Neural Network, Static Analysis, Malware Families, Deep Learning, Cybersecurity