Radio Frequency Fingerprinting through Deep Learning (RFFDL) is a data-driven
IoT authentication technique that leverages the unique hardware-level
manufacturing imperfections associated with a particular device to recognize
(fingerprint) the device based on variations introduced in the transmitted
waveform. The proposed SignCRF is a scalable, channel-agnostic, data-driven
radio authentication platform with unmatched precision in fingerprinting
wireless devices based on their unique manufacturing impairments and
independent of the dynamic channel irregularities caused by mobility. SignCRF
consists of (i) a baseline classifier finely trained to authenticate devices
with high accuracy and at scale; (ii) an environment translator carefully
designed and trained to remove the dynamic channel impact from RF signals while
maintaining the radio's specific signature; (iii) a Max-Rule module that
selects the highest precision authentication technique between the baseline
classifier and the environment translator per radio. We design, train, and
validate the performance of SignCRF for multiple technologies in dynamic
environments and at scale (100 LoRa and 20 WiFi devices). We demonstrate that
SignCRF significantly improves the RFFDL performance by achieving as high as 5x
and 8x improvement in correct authentication of WiFi and LoRa devices when
compared to the state-of-the-art, respectively.

Radio Frequency Fingerprinting 通过 Deep Learning 技术实现 IoT 设备的认证，提出了一种基于设备硬件级别制造缺陷的无线认证平台 SignCRF，可以在动态环境下精准识别 LoRa 和 WiFi 设备。