2023 · Preprint
Asteria-Pro: Enhancing Deep-Learning Based Binary Code Similarity Detection by Incorporating Domain Knowledge
Asteria-Pro adds domain-knowledge pre-filtration and re-ranking to Tree-LSTM-based binary code similarity detection. It evaluates firmware vulnerability search across diverse IoT hardware architectures.
Scope and limitations
The evaluation covers a firmware bug-search task: pre-filtration reduced calculation time by 96.9%; re-ranking improved MRR by 23.71% and Recall by 36.4%. A large-scale search found 1,482 vulnerable functions at 91.65% precision.
Why it fits this collection
It studies binary function similarity and its use in finding vulnerable code in firmware.
Publication and review details
- Venue
- arXiv
- Status
- Preprint
- Publication / posting date
- January 2, 2023
- Date source
- First arXiv submission date.
- First arXiv submission
- January 2, 2023
- arXiv
- 2301.00511
- Reviewed arXiv version
- v2
The date and venue come from the arXiv record.
Sources reviewed
Reviewed against the paper and its primary sources. Cite the original authors using the linked paper or BibTeX.