Supervised Learning for Physical Layer based Message Authentication in URLLC scenarios
This work addresses secure data transmission for URLLC applications, but it appears incremental as it applies existing supervised learning methods to a specific domain.
The paper tackles the problem of authenticating legitimate transmitters in URLLC scenarios using physical layer security, and it presents the performance of supervised learning classifiers for this task under various conditions.
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.