Deep Sequence-to-Sequence Models for GNSS Spoofing Detection
This addresses spoofing detection for GNSS systems, but it is incremental as it applies existing deep learning methods to a new dataset.
The paper tackled GNSS spoofing detection by simulating attacks globally and using deep sequence-to-sequence models, achieving a 0.16% error rate with Transformer-inspired architectures.
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%.