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%.

翻译：我们提出了一个数据生成框架，旨在模拟欺骗攻击并在全球范围内随机部署攻击场景。我们应用基于深度神经网络的模型进行欺骗检测，利用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，并使用生成的数据集进行训练。我们的结果表明，深度学习模型能够准确区分欺骗信号与真实信号，实现了高检测性能。最佳结果由采用输入早期融合的Transformer启发架构获得，其错误率仅为0.16%。