As IoT technologies mature, they are increasingly finding their way into more sensitive domains, such as Medical and Industrial IoT, in which safety and cyber-security are of great importance. While the number of deployed IoT devices continues to increase exponentially, they still present severe cyber-security vulnerabilities. Effective authentication is paramount to support trustworthy IIoT communications, however, current solutions focus on upper-layer identity verification or key-based cryptography which are often inadequate to the heterogeneous IIoT environment. In this work, we present a first step towards achieving powerful and flexible IIoT device authentication, by leveraging AI adaptive Radio Frequency Fingerprinting technique selection and tuning, at the PHY layer for highly accurate device authentication over challenging RF environments.

翻译：随着物联网技术的成熟，其正逐步渗透至医疗与工业物联网等安全敏感性领域，此类场景对设备安全与网络安全具有极高要求。尽管部署的物联网设备数量呈指数级增长，但其仍存在严重的网络安全漏洞。有效的身份认证是支撑可信工业物联网通信的关键，然而现有方案多集中于上层身份验证或基于密钥的加密技术，难以适应异构工业物联网环境。本研究首次提出通过人工智能自适应选择与调优物理层射频指纹技术，在复杂射频环境中实现高精度设备身份认证，为构建强大且灵活的工业物联网设备认证机制奠定基础。