Open AccessDeep learning based RF fingerprinting for device identification and wireless security
Author(s) -
Wu Qingyang,
Feres Carlos,
Kuzmenko Daniel,
Zhi Ding,
Yu Zhou,
Liu Xin,
‘Leo’ Liu Xiaoguang
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2018.6404
Subject(s) - computer science , wireless , identification (biology) , radio frequency , scheme (mathematics) , noise (video) , transmitter , artificial neural network , wireless network , deep learning , artificial intelligence , computer network , telecommunications , botany , biology , mathematical analysis , channel (broadcasting) , mathematics , image (mathematics)
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.