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。