Learning-Based PHY-Layer Authentication for Underwater Sensor Networks

In this letter, we propose a physical (PHY)-layer authentication framework to detect spoofing attacks in underwater sensor networks. This scheme exploits the power delay profile of the underwater acoustic channel to discriminate the sensors and applies reinforcement learning (RL) to choose the authentication parameter without being aware of the network and spoofing model. We propose an RL-based authentication scheme to provide light-weight spoofing detection and a deep RL-based authentication scheme to further improve the authentication accuracy for sinks that support deep learning. Experiment results show that this scheme improves the spoofing detection accuracy and increases the utility of the network compared with the benchmark PHY-layer authentication.