Deep learning based RF fingerprinting for device identification and wireless security

RF fingerprinting is an emerging technology for identifying hardware‐specific features of wireless transmitters and may find important applications in wireless security. In this study, the authors present a new RF fingerprinting scheme using deep neural networks. In particular, a long short‐term memory based recurrent neural network is proposed and used for automatically identifying hardware‐specific features and classifying transmitters. Experimental studies using identical RF transmitters showed very high detection accuracy in the presence of strong noise (signal‐to‐noise ratio as low as dB) and demonstrated the effectiveness of the proposed scheme.