UniASM: Binary Code Similarity Detection without Fine-tuning. (arXiv:2211.01144v2 [cs.CR] UPDATED)

Binary code similarity detection (BCSD) is widely used in various binary
analysis tasks such as vulnerability search, malware detection, clone
detection, and patch analysis. Recent studies have shown that the
learning-based binary code embedding models perform better than the traditional
feature-based approaches. In this paper, we proposed a novel transformer-based
binary code embedding model, named UniASM, to learn representations of the
binary functions. We designed two new training tasks to make the spatial
distribution of the generated vectors more uniform, …

arxiv binary code detection fine-tuning