[2603.24111] Toward a Multi-Layer ML-Based Security Framework for Industrial IoT

Computer Science > Cryptography and Security arXiv:2603.24111 (cs) [Submitted on 25 Mar 2026] Title:Toward a Multi-Layer ML-Based Security Framework for Industrial IoT Authors:Aymen Bouferroum (FUN), Valeria Loscri (FUN), Abderrahim Benslimane (LIA) View a PDF of the paper titled Toward a Multi-Layer ML-Based Security Framework for Industrial IoT, by Aymen Bouferroum (FUN) and 2 other authors View PDF Abstract: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...