Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection

Understand

The problem of cross-platform binary code similarity detection aims at detecting whether two binary functions coming from different platforms are similar or not.

• It has many security applications, including plagiarism detection, malware detection, vulnerability search, etc.
• Existing approaches rely on approximate graph matching algorithms, which are inevitably slow and sometimes inaccurate, and hard to adapt to a new task.
• To address these issues, in this work, we propose a novel neural network-based approach to compute the embedding, i.e., a numeric vector, based on the control flow graph of each binary function, then the similarity detection can be done efficiently by measuring the distance between the embeddings for two functions.