Deep learning based RF fingerprinting for device identification and wireless security

Author(s): Wu, Q; Feres, C; Kuzmenko, D; Zhi, D; Yu, Z; Liu, X; Liu, X | Abstract: © The Institution of Engineering and Technology 2018 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 −12 dB) and demonstrated the effectiveness of the proposed scheme.