{
  "added_at": "2026-11-06",
  "arxiv_id": "2301.00511",
  "arxiv_version_reviewed": "v2",
  "authors": [
    "Shouguo Yang",
    "Chaopeng Dong",
    "Yang Xiao",
    "Yiran Cheng",
    "Zhiqiang Shi",
    "Zhi Li",
    "Limin Sun"
  ],
  "bibtex_url": "https://awesomerea.org/papers/citations/paper-asteria-pro.bib",
  "code_url": null,
  "date_basis": "First arXiv submission date.",
  "date_basis_zh": "arXiv 首次提交日期。",
  "date_precision": "day",
  "description_en": "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.",
  "description_zh": "Asteria-Pro 在基于 Tree-LSTM 的二进制代码相似性检测中加入领域知识预筛选和重排序，并评估其在不同物联网硬件架构的固件漏洞搜索中的表现。",
  "doi": null,
  "entry_url": "https://awesomerea.org/papers/paper-asteria-pro/",
  "entry_url_zh": "https://awesomerea.org/zh/papers/paper-asteria-pro/",
  "evidence_urls": [
    "https://arxiv.org/abs/2301.00511"
  ],
  "first_arxiv_date": "2023-01-02",
  "id": "paper-asteria-pro",
  "json_url": "https://awesomerea.org/papers/paper-asteria-pro/record.json",
  "kind": "research",
  "markdown_url": "https://awesomerea.org/papers/paper-asteria-pro/index.md",
  "markdown_url_zh": "https://awesomerea.org/zh/papers/paper-asteria-pro/index.md",
  "metadata_notes": "The date and venue come from the arXiv record.",
  "metadata_notes_zh": "日期和发表场所来自 arXiv 记录。",
  "pdf_url": "https://arxiv.org/pdf/2301.00511",
  "project_url": null,
  "publication_date": "2023-01-02",
  "publication_status": "preprint",
  "record_type": "paper",
  "runtime_tested": false,
  "scope_limitations_en": "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.",
  "scope_limitations_zh": "评估涵盖固件漏洞搜索：预筛选将计算时间缩短 96.9%；重排序使 MRR 提升 23.71%、Recall 提升 36.4%。大规模搜索以 91.65% 的精确率发现了 1,482 个存在漏洞的函数。",
  "targets": [
    "binary",
    "firmware"
  ],
  "title": "Asteria-Pro: Enhancing Deep-Learning Based Binary Code Similarity Detection by Incorporating Domain Knowledge",
  "title_zh": "Asteria-Pro：融合领域知识提升基于深度学习的二进制代码相似性检测",
  "url": "https://arxiv.org/abs/2301.00511",
  "venue": "arXiv",
  "verification": "source-reviewed",
  "verified_at": "2026-11-06",
  "why_fits_en": "It studies binary function similarity and its use in finding vulnerable code in firmware.",
  "why_fits_zh": "论文研究二进制函数相似性检测及其在固件漏洞代码搜索中的应用。",
  "year": 2023
}