Reliable Digital Forensics in the Air: Exploring an RF-based Drone Identification System

As the drone becomes widespread in numerous crucial applications with many powerful functionalities (e.g., reconnaissance and mechanical trigger), there are increasing cases related to misused drones for unethical even criminal activities. Therefore, it is of paramount importance to identify these malicious drones and track their origins using digital forensics. Traditional drone identification techniques for forensics (e.g., RF communication, ID landmarks using a camera, etc.) require high compliance of drones. However, malicious drones will not cooperate or even spoof these identification techniques. Therefore, we present an exploration for a reliable and passive identification approach based on unique hardware traits in drones directly (e.g., analogous to the fingerprint and iris in humans) for forensics purposes. Specifically, we investigate and model the behavior of the parasitic electronic elements under RF interrogation, a particular passive parasitic response modulated by an electronic system on drones, which is distinctive and unlikely to counterfeit. Based on this theory, we design and implement DroneTrace, an end-to-end reliable and passive identification system toward digital drone forensics. DroneTrace comprises a cost-effective millimeter-wave (mmWave) probe, a software framework to extract and process parasitic responses, and a customized deep neural network (DNN)-based algorithm to analyze and identify drones. We evaluate the performance of DroneTrace with 36 commodity drones. Results show that DroneTrace can identify drones with the accuracy of over 99% and an equal error rate (EER) of 0.009, under a 0.1-second sensing time budget. Moreover, we test the reliability, robustness, and performance variation under a set of real-world circumstances, where DroneTrace maintains accuracy of over 98%. DroneTrace is resilient to various attacks and maintains functionality. At its best, DroneTrace has the capacity to identify individual drones at the scale of 104 with less than 5% error.