This research is focused on a novel approach to detect malware based on static analysis of executable files. Specifically, we treat each executable file as a twodimensional image and use robust hashing techniques to identify whether a given executable belongs to a particular family or not. The hashing stage comprises two steps, namely, feature extraction, and compression. We compare our robust hashing approach to other machine learning-based techniques.
Huang, Wei-Chung, "Image Robust Hashing for Malware Detection" (2018). Master's Projects. 625.
Available for download on Saturday, June 01, 2019