# 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.

- Authors: Shouguo Yang; Chaopeng Dong; Yang Xiao; Yiran Cheng; Zhiqiang Shi; Zhi Li; Limin Sun
- 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
- Sources reviewed: 2026-11-06
- Added to the catalog: 2026-11-06
- Original paper: <https://arxiv.org/abs/2301.00511>
- PDF: <https://arxiv.org/pdf/2301.00511>
- BibTeX: <https://awesomerea.org/papers/citations/paper-asteria-pro.bib>
- Catalog page: <https://awesomerea.org/papers/paper-asteria-pro/>
- Record JSON: <https://awesomerea.org/papers/paper-asteria-pro/record.json>
- Chinese version: [简体中文](https://awesomerea.org/zh/papers/paper-asteria-pro/index.md)

## 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 notes

The date and venue come from the arXiv record.

## Primary sources

1. <https://arxiv.org/abs/2301.00511>

## About this entry

Reviewed against the paper and its primary sources. Cite the original authors using the linked paper or BibTeX.
Original editorial content: MIT. Linked projects, papers and assets retain their own rights and licenses.