Supervised Learning for Physical Layer Based Message Authentication in URLLC Scenarios
PHYSEC based message authentication can, as an alternative to conventional security schemes, be applied within Ultra Reliable Low Latency Communication (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 Software Defined Radio (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.