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

DeepRadioID: Real-Time Channel-Resilient Optimization of Deep Learning-based Radio Fingerprinting Algorithms