Fine-grained Augmentation for RF Fingerprinting under Impaired Channels

Abstract

Critical infrastructures such as connected and au-tonomous vehicles, are susceptible to cyber attacks due to their mission-critical deployment. To ensure security by design, radio frequency (RF)-based security is considered as an effective technique for a wirelessly monitored or actuated critical infrastructure. For this purpose, this paper proposes a novel augmentation-driven deep learning approach to analyze unique transmitter fingerprints to determine the legitimacy of a user device or transmitter. An RF fingerprinting model is susceptible to various channel and environmental conditions that impact the learning performance of a machine/deep learning model. As data gathering cannot be considered as a feasible alternative, efficient solutions that can tackle the impact of varying channels on learning performance are emergent. This work aims to shed light on the RF fingerprinting problem from a different angle when 4G, 5G and WiFi data samples are collected from different transmitters by proposing a fine-grained augmentation approach to improve the learning performance of a deep learning model. Numerical results point out the promising RF fingerprinting performance when training data are augmented in a waveform-specific fine-grained manner as fingerprinting accuracy (87.94%) under the previously presented TDL/CDL augmentation can be boosted to 95.61% under previously unseen RF data instances.