In this paper we investigate the use of graph embedding networks, with unsupervised features learning, as neural architecture to learn over binary functions.
We propose several ways of automatically extract features from the control flow graph (CFG) and we use the structure2vec graph embedding techniques to translate a CFG to a vectors of real numbers. We train and test our proposed architectures on two different binary analysis tasks: binary similarity, and, compiler provenance. We show that the unsupervised extraction of features improves the accuracy on the above tasks, when compared with embedding vectors obtained from a CFG annotated with manually engineered features (i.e., ACFG proposed in [39]).
We additionally compare the results of graph embedding networks based techniques with a recent architecture that do not make use of the structural information given by the CFG, and we observe similar performances. We formulate a possible explanation of this phenomenon and we conclude identifying important open challenges.