Analysis of Augmentation Methods for RF Fingerprinting under Impaired Channels

Cyber-physical systems such as autonomous vehicle networks are considered to be critical infrastructures in various applications. However, their mission critical deployment makes them prone to cyber-attacks. Radio frequency (RF) fingerprinting is a promising security solution to pave the way for "security by design"for critical infrastructures. With this in mind, this paper leverages deep learning methods to analyze unique fingerprints of transmitters so as to discriminate between legitimate and malicious unmanned vehicles. As RF fingerprinting models are sensitive to varying environmental and channel conditions, these factors should be taken into consideration when deep learning models are employed. As another option, data acquisition can be considered; however, it is infeasible since collecting samples of different circumstances for the training set is quite difficult. To address such aspects of RF fingerprinting, this paper applies various augmentation methods, namely, additive noise, generative models and channel profiling. Out of the studied augmentation methods, our results indicate that tapped delay line and clustered delay line (TDL/CDL) models seem to be the most viable solution as the accuracy to recognize transmitters can significantly increase from 74% to 87.94% on unobserved data.