As a critical infrastructure for contemporary information technology industry, industrial internet of things (IIoT) contains a vast amount of sensitive data, making it a key requirement to ensure data security. As the use of wireless networks as a means of communication between nodes is becoming more and more common, in order to prevent malicious attacks from compromising the system, a zero-trust authentication system is necessary. In this article, we propose a comprehensive implementation framework for zero-trust verification of IIoT wireless transmission nodes, which utilizes federated learning to achieve zero-trust rule training and terminal model training, while employing blockchain technology for on-chain aggregation and cloud backup of the models. This approach enhances the accuracy and availability of the zero-trust rules while safeguarding the security of IIoT nodes. The constructed zero-trust framework incorporates a self-incremental learning function, and experiments show that it achieves a high level of accuracy at recognising attacks. Finally, we discuss the challenges of utilizing federated learning in zero-trust for IIoT and several potential solutions to address these challenges.

中文翻译：

---

工业无线互联网零信任模型：零信任遇见区块链动态联邦学习

工业物联网作为当代信息技术产业的关键基础设施，蕴藏着海量的敏感数据，保障数据安全成为关键要求。随着使用无线网络作为节点之间的通信手段变得越来越普遍，为了防止恶意攻击损害系统，零信任认证系统是必要的。在本文中，我们提出了一种工业物联网无线传输节点零信任验证的综合实现框架，利用联邦学习实现零信任规则训练和终端模型训练，同时利用区块链技术进行链上聚合和云备份。模型。这种方法增强了零信任规则的准确性和可用性，同时保护了工业物联网节点的安全。构建的零信任框架具有自增量学习功能，实验表明其在识别攻击方面达到了较高的准确率。最后，我们讨论了在工业物联网零信任中利用联邦学习的挑战以及解决这些挑战的几种潜在解决方案。