Method for Identifying Pseudo GPS Signal Based on Radio Frequency Fingerprint

Wang, Hailin; Cheng, Weihua; Xu, Chao; Zhang, Mingming; Liang, Hu

doi:10.1109/icccas.2018.8769313

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…They demonstrated the applicability of their proposed approach by stopping a spoofing attack on a Boeing 747. In [16], the authors presented a RF fingerprinting based method to detect pseudo-GPS signals by leveraging the Doppler frequency shift in the signal as a ''fingerprint''.…”

Section: Related Workmentioning

confidence: 99%

GANSAT: A GAN and SATellite Constellation Fingerprint-Based Framework for GPS Spoof-Detection and Location Estimation in GPS Deprived Environment

et al. 2022

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This paper presents a robust system for mitigating adversarial and natural GPS disruptions by presenting: (1) a software-based defense mechanism against spoofing attacks using generative adversarial networks (GANs), The system detects unauthorized or spoofed GPS signals from a hardware based spoofer, and (2) deep neural network models to infer positioning information in GPS-degraded /denied environments using the novel idea of GPS satellite constellation fingerprint. As the GAN and Satellite constellation fingerprinting are used together in a unified framework, we call it the ''GANSAT positioning system.'' Intuitively, the GANSAT neural networks implicitly learn a representation of the aggregation of the hardware fingerprints of the satellite's in the GPS constellation at a given location and time. To demonstrate the approach, raw GPS signals were collected from the satellite transmitters using a software defined radio (SDR) at five different locations in the Florida panhandle area of the United States. Additionally, a GPS spoofer is implemented using a SDR and an open source software and used in an uncontrolled laboratory environment for spoofing the GPS signals at the aforementioned locations. In our experiments, the GANSAT framework yields ∼99.5% accuracy for the task of identifying and filtering the spoofed GPS signals from real ones. It also achieves ∼100% accuracy for the task of location estimation.

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Section: Related Workmentioning

confidence: 99%