Intelligent Trajectory-based Approach to UAV Location Integrity Checks

While unmanned aerial vehicles (UAVs) are increasingly utilized in many domains, there is a growing concern about location integrity for securely deploying and managing the vehicles. A body of studies tackled this problem, e.g., using hardware sensors, cryptographic mechanisms, and machine learning (ML) approaches, but they concentrate primarily on GPS-related information (e.g., jamming and noise). In this study, we take a different approach that performs the checks by analyzing actual movement information. This new approach keeps track of location updates across the flight path (‘trajectory’) rather than relying only on point-wise GPS-specific features to test the validity of the location information. To this end, we develop a deep sequence method that takes a sequence of flight data samples with a minimal set of attributes capturing location movement over time. Our extensive experimental results support the feasibility of our approach, showing up to 98.9% accuracy for ensuring location consistency (even without referring to any of the GPS-specific features).