Massive GPS navigation services are used by billions of people in their daily lives. GPS spoofing is quite a challenging problem nowadays. Existing Anti-GPS spoofing systems primarily focus on expensive equipment and complicated algorithms, which are not practical and deployable for most of the users. In this thesis, we explore the feasibility of a simple text-based system design for Anti-GPS spoofing. The goal is to use the lower cost and make the system more effective and robust for general spoofing attack detection. Our key idea is to only use the textual information from the physical world and build a real-time system to detect GPS spoofing. To demonstrate the feasibility, we first design image processing modules to collect sufficient textual information in panoramic images. Then, we simulate real-world spoofing attacks from two cities to build our training and testing datasets. We utilize LSTM to build a binary classifier which is the key for our Anti-GPS spoofing system. Finally, we evaluate the system performance by simulating driving tests. We prove that our system can achieve more than 98% detection accuracy when the ratio of attacked points in a driving route is more than 50%. Our system has a promising performance for general spoofing attack strategies and it proves the feasibility of using textual information for the spoofing attack detection. / Master of Science / Nowadays, people are used to using GPS navigation services in their daily lives. However, GPS can be easily spoofed and the wrong GPS information will mislead victims to an unknown place. There are some existing methods that can defend GPS spoofing attacks, but all of them have significant shortcomings. Our goal is to design a novel system, which is cheap, effective, and robust, to detect general GPS spoofing attacks in real-time. In this thesis, we propose a complete system design and evaluations for performance. Our system only uses textual information from the real physical world and virtual maps. To get more accurate textual information, we use state of the art techniques for image processing and text recognition. We also use a neural network to help with detection. By testing with datasets in two cities, we confirm the promising performance of our system for general GPS spoofing attack strategies. We believe that textual information can be further developed in the Anti-GPS spoofing systems.

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