ReGraph: A Tool for Binary Similarity Identification
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866910916864376832 |
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| author | Zhou, Li Dacier, Marc Konstantinou, Charalambos |
| author_facet | Zhou, Li Dacier, Marc Konstantinou, Charalambos |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_16219 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ReGraph: A Tool for Binary Similarity Identification Zhou, Li Dacier, Marc Konstantinou, Charalambos Cryptography and Security 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. |
| title | ReGraph: A Tool for Binary Similarity Identification |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2504.16219 |