Toward a Multi-Layer ML-Based Security Framework for Industrial IoT

Computer Science > Cryptography and Security [Submitted on 25 Mar 2026] Toward a Multi-Layer ML-Based Security Framework for Industrial IoT Aymen Bouferroum (FUN), Valeria Loscri (FUN), Abderrahim Benslimane (LIA) The Industrial Internet of Things (IIoT) introduces significant security challenges as resource-constrained devices become increasingly integrated into critical industrial processes. Existing security approaches typically address threats at a single network layer, often relying on expensive hardware and remaining confined to simulation environments. In this paper, we present the research framework and contributions of our doctoral thesis, which aims to develop a lightweight, Machine Learning (ML)-based security framework for IIoT environments. We first describe our adoption of the Tm-IIoT trust model and the Hybrid IIoT (H-IIoT) architecture as foundational baselines, then introduce the Trust Convergence Acceleration (TCA) approach, our primary contribution that integrates ML to predict and mitigate the impact of degraded network conditions on trust convergence, achieving up to a 28.6% reduction in convergence time while maintaining robustness against adversarial behaviors. We then propose a real-world deployment architecture based on affordable, open-source hardware, designed to implement and extend the security framework. Finally, we outline our ongoing research toward multi-layer attack detection, including physical-layer threat identification and considerations for robustness against adversarial ML attacks. Subjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG) Cite as: arXiv:2603.24111 [cs.CR]   (or arXiv:2603.24111v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.24111 Focus to learn more Journal reference: RESSI 2026 - Rendez-vous de la Recherche et de l'Enseignement de la S{é}curit{é} des Syst{è}mes d'Information, May 2026, Clervaux, Luxembourg Submission history From: Aymen Salah Eddine Bouferroum [view email] [via CCSD proxy] [v1] Wed, 25 Mar 2026 09:16:43 UTC (2,162 KB) Access Paper: view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 Change to browse by: cs cs.LG 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?)