A Time-Series Based Convolutional VAE for Spoof Detection in Commercial GPS Receivers

Global Positioning System (GPS) technology is widely used in personal and industrial applications to acquire precise timing and positional information. However, its open-standard signals are vulnerable to spoofing attacks, which can cause serious damage if undetected. Employing detection methods is crucial in critical applications. Machine Learning (ML) methods have been successfully applied for spoofing detection, typically performing detection on individual samples. This work proposes a framework that takes a multivariate time-series window as input, enabling the neural network model to extract meaningful temporal information from the sample window for improved detection performance. We train a Convolutional Variational Autoencoder model using spoof-free samples under a representation learning framework. The detector's performance is evaluated using the publicly available TEXBAT dataset and simulated datasets. Our results show that the proposed detector achieves a True Positive Rate (TPR) above 99% for a low False Positive Rate (FPR) of 2% in both static and dynamic attack scenarios. Additionally, for the sophisticated attack scenario (DS-7) in the TEXBAT dataset, our detector achieved a TPR of 89% for an FPR of 3%, highlighting its robustness against different types of spoofing attacks.