Deep Neural Network Approach to Detect GNSS Spoofing Attacks

Parisa Borhani-Darian, Haoqing Li, Peng Wu, Pau Closas

Peer Reviewed

Abstract: This article discusses the use of deep learning schemes for spoofing detection. Particularly, the characteristics of the so-called Cross Ambiguity Function (CAF) in the presence and absence of spoofing signals are exploited to train a set of data-driven models providing a probabilistic classification. The method operates on a per-satellite basis. The results show that complex neural networks are effectively able to capture the nature of spoofing attacks. Particularly, a Multi-Layer Perceptron (MLP) and two classes of Convolution Neural Networks (CNNs) are considered in this work, validated over simulated data.
Published in: Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020)
September 21 - 25, 2020
Pages: 3241 - 3252
Cite this article: Borhani-Darian, Parisa, Li, Haoqing, Wu, Peng, Closas, Pau, "Deep Neural Network Approach to Detect GNSS Spoofing Attacks," Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020), , September 2020, pp. 3241-3252.
https://doi.org/10.33012/2020.17537
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