LoFin: LoRa-based UAV Fingerprinting Framework

The emerging proliferation of unmanned aerial vehicles (UAV) combined with their autonomous capabilities established the solid incorporation of UAVs for military applications. However, seamless deployment of drones into the adversarial environment and on the battlefield requires a robust and secure network stack, protected from adversarial intrusion. As LoRa became a low-cost solution for the long-distance control channel, it solved the challenge of long-range connectivity and prolonged lifespan present in UAV applications. However, the existing implementations lack protection mechanisms against unauthorized access. In this paper, we present LoFin, the first fingerprinting framework used to identify telemetry transceivers that communicate over the LoRa channel. LoFin exploits information leaked due to the differences in hardware structure, which results in processing time variations. Passively collecting network traffic, LoFin extracts unique signatures for UAV authentication while maintaining network integrity and data privacy. We evaluate the efficacy of our system on the extensive amount of network packets collected from the testbed of UAV transceivers in both isolated and adversarial settings. Our experiments demonstrate exceptional performance in distinguishing different LoRa-based devices with average precision and recall over 99% and 96% correspondingly.