We present a data generation framework designed to simulate spoofing attacks and randomly place attack scenarios worldwide. We apply deep neural network-based models for spoofing detection, utilizing Long Short-Term Memory networks and Transformer-inspired architectures. These models are specifically designed for online detection and are trained using the generated dataset. Our results demonstrate that deep learning models can accurately distinguish spoofed signals from genuine ones, achieving high detection performance. The best results are achieved by Transformer-inspired architectures with early fusion of the inputs resulting in an error rate of 0.16%.
@article{arxiv.2510.19890,
title = {Deep Sequence-to-Sequence Models for GNSS Spoofing Detection},
author = {Jan Zelinka and Oliver Kost and Marek Hrúz},
journal= {arXiv preprint arXiv:2510.19890},
year = {2025}
}