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 consider the binary similarity problem that consists in determining if two binary functions are similar only considering their compiled form. This problem is know to be crucial in several application scenarios, such as copyright disputes, malware analysis, vulnerability detection, etc. The current state-of-the-art solutions in this field work by creating an embedding model that maps binary functions into vectors in .

The binary similarity problem consists in determining if two functions are similar by only considering their compiled form. Techniques for binary similarity have an immediate practical impact on several fields such as copyright disputes, malware analysis, vulnerability detection, etc. Current solutions compare functions by first transforming their binary code in multi-dimensional vector representations (embeddings), and then comparing vectors through simple and efficient geometric operations.

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