CyberIntel ⬡ News
★ Saved ◆ Cyber Reads
← Back ◬ AI & Machine Learning Apr 22, 2026

API Security Based on Automatic OpenAPI Mapping

arXiv Security Archived Apr 22, 2026 ✓ Full text saved

arXiv:2604.19471v1 Announce Type: new Abstract: This paper presents Map Reduce Graph (MRG), a novel unsupervised method for modeling and securing HTTP REST APIs. MRG learns API structure from real-world traffic without prior knowledge or labels, automatically generating OpenAPI-compliant documentation by reconstructing routes, methods, and parameter formats. MRG enables real-time updates, explainable visualization, and anomaly detection, helping identify undocumented or evolving behaviors. It de

Full text archived locally
✦ AI Summary · Claude Sonnet


    Computer Science > Cryptography and Security [Submitted on 21 Apr 2026] API Security Based on Automatic OpenAPI Mapping Yarin Levi, Ran Dubin This paper presents Map Reduce Graph (MRG), a novel unsupervised method for modeling and securing HTTP REST APIs. MRG learns API structure from real-world traffic without prior knowledge or labels, automatically generating OpenAPI-compliant documentation by reconstructing routes, methods, and parameter formats. MRG enables real-time updates, explainable visualization, and anomaly detection, helping identify undocumented or evolving behaviors. It detects malformed requests, structural deviations, and injection attacks using graph-based validation and a deep autoencoder for payload analysis. Compared to state-of-the-art methods like HRAL and FT-ANN, MRG achieves up to 11.4% higher recall, over 20 times faster inference, and perfect precision (100%) on multiple API-layer attacks. Designed for dynamic microservice environments, MRG operates in three phases - training, updating, and detection - and integrates smoothly with observability and security tools. This work contributes a fully automated, efficient pipeline for real-time API visibility, schema inference, and anomaly detection without manual tuning or labeled data. Comments: none Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2604.19471 [cs.CR]   (or arXiv:2604.19471v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.19471 Focus to learn more Submission history From: Ran Dubin [view email] [v1] Tue, 21 Apr 2026 13:52:39 UTC (419 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
    💬 Team Notes
    Article Info
    Source
    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Apr 22, 2026
    Archived
    Apr 22, 2026
    Full Text
    ✓ Saved locally
    Open Original ↗