Accelerating Trust Convergence in IIoT: A ML Approach for Dynamic Network Conditions

Computer Science > Cryptography and Security [Submitted on 18 Jun 2026] Accelerating Trust Convergence in IIoT: A ML Approach for Dynamic Network Conditions Aymen Bouferroum (FUN), Valeria Loscri (FUN), Abderrahim Benslimane (LIA) In Industrial Internet of Things (IIoT) environments, trust management plays a vital role in securing systems, especially when dealing with resource-constrained devices. Traditional trust models often overlook the impact of fluctuating network quality, leading to slower trust convergence and inaccurate assessments. In this paper, we propose a dynamic trust management solution, known as the Trust Convergence Acceleration (TCA) approach, which integrates Machine Learning (ML) to accelerate trust convergence under poor network conditions. Our model predicts the number of time units needed for trust convergence based on key network metrics and dynamically adapts transition probabilities in the trust model to enhance convergence speed. Using a simulation framework that incorporates realistic Wi-Fi channel conditions based on the IEEE 802.11 standard, we demonstrate the effectiveness of the TCA-based approach, achieving up to a 28.6% reduction in trust convergence time under challenging conditions. Furthermore, the proposed solution exhibits resilience in scenarios involving malicious nodes, improving trust evaluation accuracy. This work provides a scalable and adaptive trust framework for IIoT systems in dynamic industrial environments, ensuring robust performance under varying network conditions. Comments: Symposium: Communication \& Information Systems Security (CISS) Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2606.20214 [cs.CR]   (or arXiv:2606.20214v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.20214 Focus to learn more Journal reference: IEEE Global Communications Conference (GLOBECOM) 2025, Dec 2025, Taipei, Taiwan. pp.4427-4432 Submission history From: Aymen Salah Eddine Bouferroum [view email] [via CCSD proxy] [v1] Thu, 18 Jun 2026 13:29:06 UTC (645 KB) Access Paper: view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 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?)