A deep learning approach to program similarity.

In this work we tackle the problem of binary code similarity by using deep learning applied to binary code visualization techniques. Our idea is to represent binaries as images and then to investigate whether it is possible to recognize similar binaries by applying deep learning algorithms for image classification. In particular, we apply the proposed deep learning framework to a dataset of binary code variants obtained through code obfuscation. These binary variants exhibit similar behaviours while being syntactically different. Our results show that the problem of binary code recognition is strictly separated from simple image recognition problems. Moreover, the analysis of the results of the experiments conducted in this work lead us to the identification of interesting research challenges. For example, in order to use image recognition approaches to recognize similar binary code samples it is important to further investigate how to build a suitable mapping from executables to images.