Hardware and Deep Learning-Based Authentication Through Enhanced RF Fingerprints of 3D-Printed Chaotic Antenna Arrays

Radio frequency (RF) fingerprinting is a hardware-based authentication technique utilizing the distinct distortions in the received signal due to the unique hardware differences in 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 device 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. Our results show that the enhanced RF signatures of CAA enable highly accurate authentication of hundreds of devices under practical settings.