Multi-Observation-Multi-Channel-Attribute-Based Multiuser Authentication for Industrial Wireless Edge Networks

In order to truly promote the further development of the Industrial Internet of Things (IIoT), terminal authentication of the IIoT is essential. Physical-Layer Authentication (PLA) has recently attracted much attention for its high security and lightweight. Nevertheless, most existing PLA schemes in conjunction only the observation of a single receiver will lead to low-reliability and low-robustness of authentication, especially in hostile time-varying wireless channels. To tackle this issue, we developed a multi-observation-multi-channel-attribute (MOMCA)-based multi-user authentication architecture, which considers both the observations of multi-receivers and multiple channel attributes of each observation to enhance wireless security. Specifically, the proposed architecture can provide additional spatial recognition characteristics for multi-users. To better fit the channel features of multi-observations, we proposed two Gradient Boosting Optimization (GBO)-based schemes. One uses Taylor expansion to approximate objective functions and adds the regularization term to avoid over-fitting issues. The other can obtain higher authentication performance by sampling the signal data with small gradient characteristics. The simulations on real industrial indoor and outdoor datasets verify the superiority of the proposed schemes in authentication accuracy over six baseline authentication schemes.