Tangled Program Graph for Radio-Frequency Fingerprint Identification

Alice Chillet (1) , Baptiste Boyer (1) , Robin Gerzaguet (1) , Karol Desnos (2) , Matthieu Gautier (1)

1 GRANIT - Adaptive algorithms and architectures for energy-efficient wireless systems
2 IETR - Institut d'Électronique et des Technologies du numéRique

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-theart 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.

Mots clés: Tangled Program Graph; Deep Learning; Radio Frequency Fingerprint; Software Defined Radio

Domaines: Intelligence artificielle [cs.AI]; Réseaux et télécommunications [cs.NI]

Alice Chillet, Baptiste Boyer, Robin Gerzaguet, Karol Desnos, Matthieu Gautier. Tangled Program Graph for Radio-Frequency Fingerprint Identification. 2023 Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC 2023), Sep 2023, Toronto ( CA ), France. ⟨hal-04211094⟩