GhostBuster: Detecting Misbehaving Remote ID-Enabled Drones.

Remote ID (RID) regulations soon applicable world-wide force drones to broadcast plaintext wireless messages providing, among others, their current location. However, malicious drone operators who want to stay stealthy might disclose RID messages carrying out location spoofing attacks, i.e., report forged locations, different from the actual ones. In this paper, we investigate the feasibility of using wireless localization approaches to detect drones carrying out location spoofing attacks. To this aim, we propose GhostBuster, a modular solution for detecting misbehaving RID-enabled drones, and we evaluate its performance via an extensive experimental campaign based on open-source data from actual drone flights. Through the analysis of real data in an area of $1. 5km\times 2.5km$ , we show that systems integrating multiple receivers can take advantage of multiple RID messages to verify the location reported by RID-enabled drones with a success rate of 95% up to 364 meters with 12 receivers. We also show that channel conditions play a crucial role in defining the maximum achievable spoofing detection performance.