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Communication Dans Un Congrès Année : 2023

Machine Learning Assessment of Anti-Spoofing Techniques for GNSS Receivers

Résumé

Global Navigation Satellite Systems (GNSS) are often the target of malicious attacks and interferences, mainly spoofing, thus posing a significant threat to both civilian and military equipment, and therefore necessitating effective detection and identification of such attacks. In this 'Work-in-Progress' paper, we propose the application of Machine Learning neural networks, a methodology proven highly effective in fields like cyberattack detection, to identify spoofing events across various scenarios. Our approach consists in computing non-time related metrics from a dataset of known spoofed signals, using the observables and signal-level measurements provided by a GNSS software receiver. The training is validated on both spoofed and clean scenarios to ensure a comprehensive approach. Furthermore, we provide a description of the feature's importance in the decision-making process of the model.
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hal-04184075 , version 1 (22-08-2023)

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  • HAL Id : hal-04184075 , version 1

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Gabriel Marchand, Abdelmalek Toumi, Gonzalo Seco-Granados, José A López-Salcedo. Machine Learning Assessment of Anti-Spoofing Techniques for GNSS Receivers. Work-in-Progress in Hardware and Software for Location Computation, WIHAL 2023, Jun 2023, Castellon, Spain. ⟨hal-04184075⟩
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