Internet-of-Things (IoT) devices are often used to transmit physical sensor data over digital wireless channels. Traditional Physical Layer Security (PLS)-based cryptography approaches rely on accurate channel estimation and information exchange for key generation, which irrevocably ties key quality with digital channel estimation quality. Recently, we proposed a new concept called Graph Layer Security (GLS), where digital keys are derived from physical sensor readings. The sensor readings between legitimate users are correlated through a common background infrastructure environment (e.g., a common water distribution network or electric grid). The challenge for GLS has been how to achieve distributed key generation. This paper presents a Federated multi-agent Deep reinforcement learning-assisted Distributed Key generation scheme (FD2K), which fully exploits the common features of physical dynamics to establish secret key between legitimate users. We present for the first time initial experimental results of GLS with federated learning, achieving considerable security performance in terms of key agreement rate (KAR), and key randomness.

翻译：物联网设备常通过数字无线信道传输物理传感数据。传统基于物理层安全的密码学方法依赖精确的信道估计和信息交换进行密钥生成，这不可避免地使密钥质量与数字信道估计质量深度绑定。近期我们提出名为"图层安全"（GLS）的新概念，该方法从物理传感器读数中派生数字密钥。合法用户间的传感器读数通过共同的基础设施环境（如共享的供水管网或电网）产生相关性。GLS面临的挑战在于如何实现分布式密钥生成。本文提出联邦多智能体深度强化学习辅助的分布式密钥生成方案（FD2K），该方案充分利用物理动力学的共性特征，在合法用户间建立对称密钥。我们首次呈现了融合联邦学习的GLS初始实验结果，在密钥一致性率和密钥随机性方面均达到可观的安全性能。