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RefinementEngine: Automating Intent-to-Device Filtering Policy Deployment under Network Constraints

arXiv Security Archived Apr 03, 2026 ✓ Full text saved

arXiv:2604.01627v1 Announce Type: new Abstract: Translating security intent into deployable network enforcement rules and maintaining their effectiveness despite evolving cyber threats remains a largely manual process in most Security Operations Centers (SOCs). In large and heterogeneous networks, this challenge is complicated by topology-dependent reachability constraints and device-specific security control capabilities, making the process slow, error-prone, and a recurring source of misconfig

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✦ AI Summary · Claude Sonnet


    Computer Science > Cryptography and Security [Submitted on 2 Apr 2026] RefinementEngine: Automating Intent-to-Device Filtering Policy Deployment under Network Constraints Davide Colaiacomo, Chiara Bonfanti, Cataldo Basile Translating security intent into deployable network enforcement rules and maintaining their effectiveness despite evolving cyber threats remains a largely manual process in most Security Operations Centers (SOCs). In large and heterogeneous networks, this challenge is complicated by topology-dependent reachability constraints and device-specific security control capabilities, making the process slow, error-prone, and a recurring source of misconfigurations. This paper presents RefinementEngine, an engine that automates the refinement of high-level security intents into low-level, deployment-ready configurations. Given a network topology, devices, and available security controls, along with high-level intents and Cyber Threat Intelligence (CTI) reports, RefinementEngine automatically generates settings that implement the desired intent, counter reported threats, and can be directly deployed on target security controls. The proposed approach is validated through real-world use cases on packet and web filtering policies derived from actual CTI reports, demonstrating both correctness, practical applicability, and adaptability to new data. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2604.01627 [cs.CR]   (or arXiv:2604.01627v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.01627 Focus to learn more Submission history From: Chiara Bonfanti [view email] [v1] Thu, 2 Apr 2026 05:09:38 UTC (518 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.AI 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?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Apr 03, 2026
    Archived
    Apr 03, 2026
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