Hardware and deep learning based authentication through enhanced RF fingerprinting

Abstract

Radio frequency (RF) fingerprinting is a hardware-based authentication technique based on distinct distortions in the received signal due to the unique hardware differences of the transmitting device. Existing RF fingerprinting methods only utilize the naturally occurring hardware imperfections during fabrication, hence their authentication accuracy is limited in practical settings even when state-of-the-art deep learning classifiers are used. In this work, we propose a Chaotic Antenna Array (CAA) system for significantly enhanced RF fingerprints and a deep learning-based authentication method for CAA. We provide a mathematical model for CAA, explain how it can be cost effectively manufactured by utilizing mask-free laser-enhanced direct print additive manufacturing (LE-DPAM), and comprehensively analyze the authentication performance of several deep learning classifiers for CAA.

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