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.

本文针对物联网中基于物理层安全的密码学方法，提出了一种名为GLS的新概念，并介绍了一种利用分布式深度强化学习辅助的联邦式多智能体分布式密钥生成方案(FD2K)，实现了密钥协议率和密钥随机性方面的可观安全性能。

使用物理传感器数据保障物联网通信 -- 基于图层安全加强的联合多智能体深度强化学习