用深度模型识别卫星欺骗信号，准确率超99.8%
Deep Sequence-to-Sequence Models for GNSS Spoofing Detection
- 用生成框架模拟全球随机欺骗场景，构建训练数据
- Transformer类模型融合输入信息，误报率低至0.16%
- 适合实时监控卫星信号安全的工程与安全研究者
我们提出一种数据生成框架，用于模拟全球范围内的欺骗攻击场景，并应用基于深度神经网络的检测模型，包括长短期记忆网络和受Transformer启发的架构。这些模型专为在线检测设计，使用生成的数据集进行训练。实验表明，深度学习模型能准确区分欺骗信号与真实信号，性能优异。最佳结果由早期融合输入的Transformer类架构实现，错误率仅为0.16%。
原文摘要 · Abstract (English)
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%.
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