{
  "added_at": "2026-10-20",
  "arxiv_id": null,
  "arxiv_version_reviewed": null,
  "authors": [
    "Yanyang Zhao",
    "Zhengxiong Luo",
    "Wenlong Zhang",
    "Feifan Wu",
    "Yuanliang Chen",
    "Fuchen Ma",
    "Qi Xu",
    "Heyuan Shi",
    "Yu Jiang"
  ],
  "bibtex_url": "https://awesomerea.org/papers/citations/paper-transre.bib",
  "citation": {
    "container_title": "Proceedings of the ACM on Software Engineering",
    "issue": "FSE",
    "pages": "636-656",
    "publisher": "Association for Computing Machinery (ACM)",
    "sources": [
      "https://api.crossref.org/works/10.1145/3797124"
    ],
    "volume": "3"
  },
  "code_url": null,
  "date_basis": "ACM-deposited Crossref print/online publication date; the author-hosted manuscript retains placeholder publication metadata.",
  "date_basis_zh": "采用 ACM 向 Crossref 登记的纸质／在线出版日期，作者托管手稿仍保留占位出版元数据。",
  "date_precision": "day",
  "description_en": "Transfers field-boundary patterns from standardized protocol traces labeled by Tshark to poorly documented target protocols. A source-selection stage chooses related protocols, and a neural domain-adaptation model infers message syntax from their shared patterns.",
  "description_zh": "将 Tshark 标注的标准协议流量中的字段边界模式迁移到文档不足的目标协议。先选择相关源协议，再通过神经网络领域适应模型依据共享模式推断消息语法。",
  "doi": "10.1145/3797124",
  "entry_url": "https://awesomerea.org/papers/paper-transre/",
  "entry_url_zh": "https://awesomerea.org/zh/papers/paper-transre/",
  "evidence_urls": [
    "https://api.crossref.org/works/10.1145/3797124",
    "https://fcorleone.github.io/publications/",
    "https://conf.researchr.org/details/fse-2026/fse-2026-research-papers/77/Protocol-Reverse-Engineering-via-Deep-Transfer-Learning"
  ],
  "first_arxiv_date": null,
  "id": "paper-transre",
  "json_url": "https://awesomerea.org/papers/paper-transre/record.json",
  "kind": "research",
  "markdown_url": "https://awesomerea.org/papers/paper-transre/index.md",
  "markdown_url_zh": "https://awesomerea.org/zh/papers/paper-transre/index.md",
  "metadata_notes": "June 2026 is a strong six-month-window supplement. Fulltext was read from the public PDF explicitly linked by coauthor Fuchen Ma; its February date and placeholder DOI are not used as final metadata. The publisher PDF previously returned 403. Crossref confirms nine authors, volume 3(FSE), pages 636–656 and CC BY 4.0 for the version of record. No author-linked implementation release or code license was verified.",
  "metadata_notes_zh": "该工作发表于 2026 年 6 月，作为近六个月的强相关补充。全文来自共同作者 Fuchen Ma 明确链接的公开 PDF，不采用其中二月日期或占位 DOI 作为最终元数据；出版商 PDF 此前返回 403。Crossref 确认九位作者、卷 3(FSE)、636–656 页及正式版本 CC BY 4.0 许可。未核实作者关联的实现发布或代码许可。",
  "pdf_url": "https://dl.acm.org/doi/pdf/10.1145/3797124",
  "project_url": null,
  "publication_date": "2026-06-30",
  "publication_status": "journal-article",
  "record_type": "paper",
  "runtime_tested": false,
  "scope_limitations_en": "Recovers message syntax, not state transitions. Evaluation uses 12 protocol variants; payload noise can distort source selection, while encrypted, compressed or obfuscated traffic needs separate preprocessing. Camera experiments supplement format metrics, but the four reported issues were vendor-known and do not establish complete protocol recovery.",
  "scope_limitations_zh": "恢复的是消息语法，而非状态转换。评估采用 12 种协议变体；载荷噪声可能干扰源协议选择，加密、压缩或混淆流量需要另行预处理。摄像头实验补充格式指标，但四个报告问题为厂商已知问题，也不证明协议已完整恢复。",
  "targets": [
    "protocol"
  ],
  "title": "Protocol Reverse Engineering via Deep Transfer Learning",
  "title_zh": "通过深度迁移学习进行协议逆向工程",
  "url": "https://doi.org/10.1145/3797124",
  "venue": "Proceedings of the ACM on Software Engineering / FSE 2026",
  "verification": "source-reviewed",
  "verified_at": "2026-10-20",
  "why_fits_en": "Adds a transfer-learning approach to recovering undocumented message formats without parser source code.",
  "why_fits_zh": "补充无需解析器源码、利用迁移学习恢复未公开消息格式的方法。",
  "year": 2026
}