Cellular-connected Unmanned Aerial Vehicle (UAV) systems are a promising paradigm in the upcoming 5G-and-beyond mobile cellular networks by delivering numerous applications, such as the transportation of medicine, building inspection, and emergency communication. With the help of a cellular communication system and Global Navigation Satellite System (GNSS), UAVs can be deployed independently or collectively in remote and densely populated areas on demand. However, the civil GNSS service, especially GPS, is unencrypted and vulnerable to spoofing attacks, which threatens the security of remotely- and autonomously-controlled UAVs. Indeed, a GPS spoofer can use commercial Software-Defined Radio (SDR) tools to generate fake GPS signals and fool the UAV GPS receiver to calculate the wrong locations. Fortunately, the 3rd Generation Partnership Project (3GPP) has initiated a set of techniques and supports that enable mobile cellular networks to track and identify UAVs in order to enhance low-altitude airspace security. The research works in this thesis leverage the potential of machine learning methods and 3GPP technique support to detect and mitigate GPS spoofing attacks for cellular-connected UAVs. The contributions of this thesis contain four parts. First, we propose a new adaptive trustable residence area algorithm to improve the conventional Mobile Positioning System (MPS) in terms of GPS spoofing detection accuracy under three base stations. Then, we deploy deep neural networks in the Multi-access Edge Computing (MEC) based 5G-assisted Unmanned Aerial System (UAS) for detecting GPS spoofing attacks, where the proposed deep learning methods can detect GPS spoofing attacks with only a single base station. Next, we analyze the max-min transmission rate for cellular-connected UAVs system theoretically and design an optimal Graphic Neural Network (GNN) to detect GPS spoofing attacks for cellular-connected UAV swarms. Finally, we employ a 3D radio map and particle filter to recover the UAV position and mitigate GPS spoofing attacks.
|Julkaisun otsikon käännös||Machine Learning based GNSS Spoofing Detection and Mitigation for Cellular-Connected UAVs|
|Tila||Julkaistu - 2023|