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BinMLM: Binary Authorship Verification with Flow-aware Mixture-of-Shared Language Model
[article]
2022
arXiv
pre-print
Binary authorship analysis is a significant problem in many software engineering applications. In this paper, we formulate a binary authorship verification task to accurately reflect the real-world working process of software forensic experts. It aims to determine whether an anonymous binary is developed by a specific programmer with a small set of support samples, and the actual developer may not belong to the known candidate set but from the wild. We propose an effective binary authorship
arXiv:2203.04472v1
fatcat:w46qme7zr5eklkhavu4aszfngi