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LSTM-based GNSS Spoofing Detection Using Low-cost Spectrum Sensors

Calvo-Palomino, Roberto and Bhattacharya, Arani and Bovet, Gerome and Giustiniano, Domenico (2020) LSTM-based GNSS Spoofing Detection Using Low-cost Spectrum Sensors. In: The 21st IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM 2020), 31 August - 03 September 2020 , Cork, Ireland.

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GNSS/GPS is a positioning system widely used nowadays in our lives for real-time localization in Earth. This technology is highly vulnerable to spoofing/jamming attacks caused by malicious intruders. In the recent years, commodity and low-cost radio-frequency hardware have been used to interfere with the legitimate GPS signal. Existing spoofing detection solutions use costly receivers and computationally expensive algorithms which limit the large-scale deployment. In this work we propose a GNSS spoofing detection system that can run on spectrum sensors with Software-Defined Radio (SDR) capabilities and cost in the order of 20 euros. Our approach exploits the predictability of the Doppler characteristics of the received GPS signals to determine the presence of anomalies or malicious attackers. We propose an artificial recurrent neural network (RNN) based on Long short-term memory (LSTM) for anomaly detection. We use data received by low-cost SDR receivers that are processed locally by low-cost embedded machines such as Nvidia Jetson Nano to provide inference capabilities. We show that our solution predicts very accurately the Doppler shift of GNSS signals and can determine the presence of a spoofing transmitter.

Item Type: Conference or Workshop Papers (Paper)
Depositing User: Roberto Calvo
Date Deposited: 09 Jun 2020 10:10
Last Modified: 06 Nov 2020 16:38

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