MAL2IMAGE: Hybrid Image Transformation for Malware Classification
The work described in this website has been conducted within the project NeCS. This project has received funding from the European Union’s Horizon 2020 (H2020) research and innovation programme under the Grant Agreement no 675320. This website and the content displayed in it do not represent the opinion of the European Union, and the European Union is not responsible for any use that might be made of its content.
Ly Vu Duc (Universita Degli Studi Di Trento)
Existing image transformation approaches (e.g. Nataraj et al. , Liu 2016 ) 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.
16th Conference on Detection of Intrusions and Malware & Vulnerability Assessment