TRUeIDS: A Unified IoMT Intrusion Detection Framework with Zadeh’s Trust Fuzziness and R3GAN Reputation Robustness

TRUeIDS: A Unified IoMT Intrusion Detection Framework with Zadeh’s Trust Fuzziness and R3GAN Reputation Robustness | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article TRUeIDS: A Unified IoMT Intrusion Detection Framework with Zadeh’s Trust Fuzziness and R3GAN Reputation Robustness Vaishali Meena, Gaurav Indra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7686839/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Healthcare networks built on the Internet of Medical Things (IoMT) face heightened risks not only from conventional DoS, DDoS, spoofing, and reconnaissance attacks, but also from advanced adversarial attacks—including evasion, mimicry, history poisoning, membership inference, and model inversion—that most state-of-the-art IDS fail to address. To overcome these limitations, this paper presents TRUeIDS, a three-stage novel intrusion detection framework that strengthens security through integrated transient trust index and cumulative reputation learning. Stage I estimates short-term device trust using Zadeh’s Interval Type-2 Fuzzy Logic (IT2FL) to effectively manage uncertainty and adversarial noise. Stage II employs Regularized Residual Robust Generative Adversarial Network (R3GAN)-based reputation modeling to capture long-term stability, variability, and resistance to on–off behaviors. Stage III introduces a Zero-Trust decision mechanism that normalizes and fuses trust and reputation scores into adaptive thresholds, classifying devices into Allow, Challenge, or Deny states. Experiments conducted on the CICIoMT2024 dataset across binary, 6-class, and 19-class scenarios demonstrate robust performance, achieving accuracies above 98.9%, MCC up to 0.9852, and ROC-AUC consistently beyond 99.2%. These results confirm that TRUeIDS provides a mathematically rigorous and practically validated solution for secure, scalable, and resilient intrusion detection in IoMT environments. IoMT Security Trust and Reputation Intrusion Detection System (IDS) Zero Trust Architecture (ZTA) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Dec, 2025 Reviews received at journal 04 Dec, 2025 Reviewers agreed at journal 20 Nov, 2025 Reviews received at journal 13 Nov, 2025 Reviewers agreed at journal 24 Oct, 2025 Reviewers invited by journal 22 Oct, 2025 Editor assigned by journal 16 Oct, 2025 Submission checks completed at journal 23 Sep, 2025 First submitted to journal 22 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7686839","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":538583471,"identity":"84264c6e-70cf-48b1-9977-83c59e9c6cea","order_by":0,"name":"Vaishali Meena","email":"","orcid":"","institution":"Indira Gandhi Delhi technical University for Women","correspondingAuthor":false,"prefix":"","firstName":"Vaishali","middleName":"","lastName":"Meena","suffix":""},{"id":538583472,"identity":"1431fc63-69f1-4121-a63d-8b6bf83f08b1","order_by":1,"name":"Gaurav 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