Supervised Learning for Physical Layer based Message Authentication in URLLC scenarios

TL;DR

This paper explores the use of supervised learning classifiers for message authentication at the physical layer in URLLC scenarios, focusing on data collection, processing, and performance evaluation.

Contribution

It introduces a methodology for data collection and processing using SDR platforms and evaluates supervised learning classifiers for PHYSEC in URLLC.

Findings

Supervised classifiers can effectively discriminate legitimate from illegitimate transmitters.

Data collection and preprocessing pipeline is established for PHYSEC in URLLC.

Performance varies under different side conditions.

Abstract

PHYSEC based message authentication can, as an alternative to conventional security schemes, be applied within \gls{urllc} scenarios in order to meet the requirement of secure user data transmissions in the sense of authenticity and integrity. In this work, we investigate the performance of supervised learning classifiers for discriminating legitimate transmitters from illegimate ones in such scenarios. We further present our methodology of data collection using \gls{sdr} platforms and the data processing pipeline including e.g. necessary preprocessing steps. Finally, the performance of the considered supervised learning schemes under different side conditions is presented.