2025/04/22 by L. P. Zhou, Zhou, Li, Marc Daciér +3 · 1 citation
Computer Science · #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Semantic Web and Ontologies
paper · pdf · doi:10.48550/arxiv.2504.16219
openalex publication_date 2025/04/22 · openalex created_date 2025/10/11 · openalex updated_date 2026/07/28
Binary Code Similarity Detection (BCSD) is not only essential for security tasks such as vulnerability identification but also for code copying detection, yet it remains challenging due to binary stripping and diverse compilation environments. Existing methods tend to adopt increasingly complex neural networks for better accuracy performance. The computation time increases with the complexity. Even with powerful GPUs, the treatment of large-scale software becomes time-consuming. To address these issues, we present a framework called ReGraph to efficiently compare binary code functions across architectures and optimization levels. Our evaluation with public datasets highlights that ReGraph exhibits a significant speed advantage, performing 700 times faster than Natural Language Processing (NLP)-based methods while maintaining comparable accuracy results with respect to the state-of-the-art models.