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.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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