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This paper introduces Phantom Navigator, a novel UAV redirection attack that enhances precision and stealth in diverting unmanned aerial vehicles from their intended paths. By integrating offline pre-redirection reachability analysis with an online closed-loop execution layer, the method achieves high-fidelity estimates and real-time tracking to guide drones to specific targets without detection. The effectiveness of this approach is validated through real-world case studies, showcasing its potential to outperform existing redirection techniques that often lack reliability and accuracy.
Phantom Navigator achieves covert and precise UAV redirection, overcoming the limitations of traditional attack methods that are often costly and unreliable.
Redirecting unmanned aerial vehicles (UAVs) from their intended mission trajectories has been an active area of research. However, existing UAV redirection attacks lack reliability, precision, and covertness for a targeted diversion. They primarily rely on physical capture, communication hijacking, or sensor spoofing. Yet, physical interception is costly, offers only a single opportunity for success, and poses a high risk of collateral damage; network-based attacks demand deep technical expertise and access to encrypted communication channels; and sensor spoofing techniques typically fall short in achieving the accuracy and robustness required to steer a UAV toward a specified target. Consequently, we propose Phantom Navigator, a UAV redirection attack to mislead drones to a designated spoofing target, covertly and precisely. Our approach combines offline pre-redirection reachability analysis, which provides high-fidelity estimates of achievable redirect ranges, with an online closed-loop, stealthy execution layer that ensures successful redirection in practice. Based on this approach, we build a physical attack platform equipped with a LiDAR--camera detection, tracking, and spoofing stack that performs real-time identification, pose estimation, and computation of targeted spoofing signals to covertly and accurately redirect victim UAVs to a designated location. We demonstrate the effectiveness of our redirection methodology and the attack implementation in real-world case studies.
