3D Radio Map-Based GPS Spoofing Detection and Mitigation for Cellular-Connected UAVs

With the upcoming 5G and beyond wireless communication system, cellular-connected Unmanned Aerial Vehicles (UAVs) are emerging as a new pattern to give assistance for target searching, emergency rescue, and network recovery. Such text cellular-connected UAV systems highly rely on accurate and secure navigation systems, e.g. the Globe Navigation System (GPS). However, civil GPS services are unencrypted and vulnerable to spoofing attacks that can manipulate UAVs’ location and abort the UAVs’ mission. This paper leverage 3D radio map and machine learning methods to detect and mitigate GPS spoofing attacks for cellular-connected UAVs. Precisely, the edge UAV flight controller uses ray tracing tools deterministic channel models, and Kriging methods to construct a theoretical 3D radio map. Then the machine learning methods, such as text Multi-Layer Perceptrons (MLP), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), are employed to detect GPS spoofing by analyzing the UAV/base station reported Received Signal Strength (RSS) values and the theoretical radio map RSS values. Once spoofing is detected, the particle filter is applied to relocate the UAV and mitigate GPS deviation. The experiment results indicate that the Universal Kriging (UK) with exponential covariance function has the lowest standard errors for radio map construction. Moreover, the MLP achieves the highest spoofing detection accuracy with different spoofing margins because of the statistic prepossessing relieving environmental impacts, while the CNN has a comparable detection accuracy with less training time than MLP since CNN inputs are raw RSS data. Furthermore, the particle text filter-based GPS spoofing mitigation can relocate the UAV to the real position within an error of 10 meters using 100 particles.