  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.
