Privacy-Preserving Verification and Root-Cause Tracing towards UAV Social Networks

Unmanned Aerial Vehicles (UAV) have rapidly developed and been widely applied to military and civilian applications in recent years. Anomaly Detections and finding out the root causes are critically important for UAV social network security. In the UAV social networks, the drone can communicate with one another directly in a form of leading flights with followers during a far away mission. The ground controller cannot get their information directly. Besides, none of the works consider the privacy protection and anomaly root cause tracing during the distributed detection. This paper presents a self-verification approach among UAV flights which can check whether the flights have honestly obeyed the orders or suffered the anomalies. Besides, we do the verification without looking through the plain-text records or data of the drones. Finally, to instruct the drones to solve the problems, we trace the fundamental root causes leading to the anomalies by learning the fault tree. We apply our approach on raw UAV social network data and align our experiment with two former works as baselines for comparison. Our approach can reduce the time cost of verification from exponential growth to linear growth and improve the tracing accuracy rate around 43% higher than the former work.