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.

翻译：工业物联网面临严峻的安全挑战：资源受限设备日益集成到关键工业流程中，而现有安全方法通常仅针对单一网络层应对威胁，且依赖昂贵硬件，局限于仿真环境。本文阐述博士论文的研究框架与贡献，旨在为工业物联网环境开发轻量级、基于机器学习的安全框架。我们首先采用Tm-IIoT信任模型与混合工业物联网（H-IIoT）架构作为基础基线，随后引入核心贡献——信任收敛加速（TCA）方法，该方法集成机器学习以预测并缓解网络性能劣化对信任收敛的影响，在保持对抗恶意行为鲁棒性的前提下，实现收敛时间最多降低28.6%。接着，我们提出基于低成本开源硬件的实际部署架构，以实施并扩展该安全框架。最后，我们概述面向多层攻击检测的持续研究，包括物理层威胁识别及针对对抗性机器学习攻击的鲁棒性考量。