Multi-Feature Physical Layer Authentication for URLLC based on Linear Supervised Learning

Physical Layer Authentication (PLA) can be a lightweight alternative to conventional security schemes such as certificates or Message Authentication Codes (MACs) for secure message transmission within Ultra Reliable Low Latency Communication (URLLC) scenarios. Single features such as Received Signal Strength Indicator (RSSI) are however not providing sufficient authentication accuracy. Therefore, multi-feature techniques for PLA are introduced within this work and evaluated using a Universal Software Radio Peripheral (USRP) based testbed in a mobile URLLC campus network scenario. Linear supervised classification is proposed for PLA and evaluated under different attacker scenarios. The results show promising authentication performances in most of the evaluated senarions and can be increased by the application of multi-feature authentication.