MAL2IMAGE: Hybrid Image Transformation for Malware Classification

Poster

Existing image transformation approaches (e.g. Nataraj et al. [1], Liu 2016 [2]) for malware detection only perform simple transformation methods that have not considered color encoding and pixel rendering techniques on the performance of machine learning classifiers.

Aims of the research: We propose a new approach to encode and arrange bytes from a binary file into images. These developed images contain statistical (e.g., entropy) and syntactic artifacts (e.g., strings) and their pixels are filled up using Hilbert curves.