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。

SignCRF: 可扩展的无渠道信息认证射频数据驱动系统

SignCRF: Scalable Channel-agnostic Data-driven Radio Authentication System

Radio fingerprinting provides a reliable and energy-efficient IoT
authentication strategy. By mapping inputs onto a very large feature space,
deep learning algorithms can be trained to fingerprint large populations of
devices operating under any wireless standard. One of the most crucial
challenges in radio fingerprinting is to counteract the action of the wireless
channel, which decreases fingerprinting accuracy significantly by disrupting
hardware impairments. On the other hand, due to their sheer size, deep learning
algorithms are hardly re-trainable in real-time. Another aspect that is yet to
be investigated is whether an adversary can successfully impersonate another
device fingerprint. To address these key issues, this paper proposes
DeepRadioID, a system to optimize the accuracy of deep-learning-based radio
fingerprinting algorithms without retraining the underlying deep learning
model. We extensively evaluate DeepRadioID on a experimental testbed of 20
nominally-identical software-defined radios, as well as on two datasets made up
by 500 ADS-B devices and by 500 WiFi devices provided by the DARPA RFMLS
program. Experimental results show that DeepRadioID (i) increases
fingerprinting accuracy by about 35%, 50% and 58% on the three scenarios
considered; (ii) decreases an adversary's accuracy by about 54% when trying to
imitate other device fingerprints by using their filters; (iii) achieves 27%
improvement over the state of the art on a 100-device dataset.

本文提出 DeepRadioID，一个系统，其无需重新训练深度学习模型即可优化基于深度学习的无线电指纹识别算法的准确性，实验结果显示其在三种情况下分别增加了 35％，50％和 58％的指纹识别准确性，并在 100 个设备数据集上实现了 27％的改进。