Hierarchical Security Authentication with Attention-Enhanced Convolutional Network for Internet of Things

As security authentication issues continue to arise in future wireless communication networks, researchers are working hard to further improve authentication techniques. Recently, physical layer authentication (PLA) has received widespread attention for its lightweight nature compared to traditional encryption methods based on keys and blockchain. However, the existing PLA mechanisms based on a fixed decision threshold have low reliability in dynamic environments. Moreover, PLA solutions are typically based on binary authentication, and these binary-type schemes cannot provide different levels of access control. To address these challenges, this article introduces the concept of hierarchical security authentication, aiming to achieve multi-level secure authorization access. In order to further improve the accuracy of identity verification, we design an Attention-Enhanced Convolutional Network (AECN) model that integrates the attention mechanism. Specifically, by introducing a confidence score branch, the proposed AECN-based PLA scheme completes authentication without a threshold, thus avoiding the issues stemming from inappropriate threshold settings in conventional PLA schemes. The simulation results show that our proposed AECN framework outperforms existing algorithms at different levels of security authentication.