{
  "added_at": "2026-11-06",
  "arxiv_id": "2108.06082",
  "arxiv_version_reviewed": "v1",
  "authors": [
    "Shouguo Yang",
    "Long Cheng",
    "Yicheng Zeng",
    "Zhe Lang",
    "Hongsong Zhu",
    "Zhiqiang Shi"
  ],
  "bibtex_url": "https://awesomerea.org/papers/citations/paper-asteria.bib",
  "code_url": null,
  "date_basis": "Published date in the Crossref record for the DOI listed on arXiv.",
  "date_basis_zh": "arXiv 所列 DOI 的 Crossref 记录中的发表日期。",
  "date_precision": "month",
  "description_en": "ASTERIA uses a Tree-LSTM to encode function ASTs and compare binary functions across platforms. It was evaluated on function pairs and used to search IoT firmware for vulnerabilities.",
  "description_zh": "ASTERIA 使用 Tree-LSTM 编码函数的抽象语法树，以比较不同平台上的二进制函数。论文评估了函数对的相似性检测，并将其用于物联网固件漏洞搜索。",
  "doi": "10.1109/dsn48987.2021.00036",
  "entry_url": "https://awesomerea.org/papers/paper-asteria/",
  "entry_url_zh": "https://awesomerea.org/zh/papers/paper-asteria/",
  "evidence_urls": [
    "https://arxiv.org/abs/2108.06082",
    "https://api.crossref.org/works/10.1109/DSN48987.2021.00036"
  ],
  "first_arxiv_date": "2021-08-13",
  "id": "paper-asteria",
  "json_url": "https://awesomerea.org/papers/paper-asteria/record.json",
  "kind": "research",
  "markdown_url": "https://awesomerea.org/papers/paper-asteria/index.md",
  "markdown_url_zh": "https://awesomerea.org/zh/papers/paper-asteria/index.md",
  "metadata_notes": "The DOI comes from the arXiv record, and the venue and date from Crossref.",
  "metadata_notes_zh": "DOI 来自 arXiv 记录，发表场所和日期来自 Crossref。",
  "pdf_url": "https://arxiv.org/pdf/2108.06082",
  "project_url": null,
  "publication_date": "2021-06",
  "publication_status": "conference-paper",
  "record_type": "paper",
  "runtime_tested": false,
  "scope_limitations_en": "The model was trained on 1,022,616 function pairs and evaluated on 95,078 pairs. Its cross-platform scope covers differing IoT architectures; a firmware search identified 75 vulnerable functions in 5,979 images.",
  "scope_limitations_zh": "模型使用 1,022,616 对函数训练，并在 95,078 对函数上评估。跨平台范围涉及不同的物联网架构；固件搜索在 5,979 个镜像中识别出 75 个存在漏洞的函数。",
  "targets": [
    "binary",
    "firmware"
  ],
  "title": "Asteria: Deep Learning-based AST-Encoding for Cross-platform Binary Code Similarity Detection",
  "title_zh": "Asteria：基于深度学习的抽象语法树编码，用于跨平台二进制代码相似性检测",
  "url": "https://doi.org/10.1109/DSN48987.2021.00036",
  "venue": "2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)",
  "verification": "source-reviewed",
  "verified_at": "2026-11-06",
  "why_fits_en": "It studies reverse engineering through binary function similarity and vulnerability search in IoT firmware.",
  "why_fits_zh": "论文研究二进制函数相似性检测及物联网固件中的漏洞搜索，属于逆向工程相关研究。",
  "year": 2021
}