1. Deep learning based RF fingerprinting for device identification and wireless security
- Author
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Wu, Q, Feres, C, Kuzmenko, D, Zhi, D, Yu, Z, and Liu, X
- Subjects
neural nets ,recurrent neural nets ,learning ,classifying transmitters ,experimental studies ,identical RF transmitters ,deep learning ,RF fingerprinting ,device identification ,wireless security ,emerging technology ,hardware-specific features ,wireless transmitters ,important applications ,deep neural networks ,short-term memory ,recurrent neural network ,Electrical And Electronic Engineering ,Artificial Intelligence And Image Processing ,Communications Technologies ,Electrical & Electronic Engineering ,Electrical and Electronic Engineering ,Artificial Intelligence and Image Processing - Abstract
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.
- Published
- 2018