Ricerc@Sapienza

The binary similarity problem consists in determining if two functions are similar considering only their compiled form. Advanced techniques for binary similarity recently gained momentum as they can be applied in several fields, such as copyright disputes, malware analysis, vulnerability detection, etc. In this paper we describe SAFE, a novel architecture for function representation based on a self-attentive neural network.

In this paper we investigate the use of graph embedding networks, with unsupervised features learning, as neural architecture to learn over binary functions.