# Deep Sequence-to-Sequence Models for GNSS Spoofing Detection

arXiv: https://arxiv.org/abs/2510.19890 · HTML: https://mlsanity.com/paper/2510.19890/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 22 Oct 2025.

## Builds on (most cited first, 2 of 2)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Learning for GPS Spoofing Detection in Cellular Enabled Unmanned Aerial Vehicle Systems](https://mlsanity.com/paper/2201.00568/) (2022)

## Abstract (arXiv)

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%.