Tangled Program Graph for Radio-Frequency Fingerprint Identification

This paper proposes to use Tangled Program Graph (TPG) for Radio Frequency Fingerprint (RFF) identification. RFF is a unique signature created by electromagnetic distortions of the different radio frequency hardware components in the device. This signature is contained in the signal and may be used as a secure identifier because it can not be easily spoofed. In recent years, RFF identification is mainly based on Deep Learning (DL). TPG is a new machine learning technique based on genetic evolution, which are less complex than DL. In this paper, we propose to use TPG-based classification to achieve a lightweight and accurate RFF identification scheme. Results show that TPGs achieve the same accuracy as a state-of-the-art convolutional neural network with a learning phase duration clearly reduced on the CPU. TPGs are also used to analyse both the impact of the channel and the receiver radio on the accuracy.