A real-time GNSS time spoofing detection framework based on feature processing

Currently, the susceptibility of Global Navigation Satellite System (GNSS) signals underscores the importance of accurate GNSS time spoofing detection as a critical research area. Traditional spoofing detection methods have limitations in applicability, while the current learning-based algorithms are only applicable to the judgment of collected data, which is difficult to apply to real-time detection. In this paper, a real-time spoofing detection framework based on feature processing is proposed. The approach involves feature integration and correlation coefficient screening on each epoch of multi-satellite data. Additionally, special standardization strategy is employed to enhance the feasibility of real-time application. In the experimental phase, apart from utilizing the open dataset, an experimental platform is developed to generate dual-system data for experimentation purposes. Compared with the traditional clock difference detection method, this algorithm improves the detection performance by about 25%. Furthermore, the framework proposed can improve the detection F1 score of basic machine learning models and greatly reduce the computation time by more than ten times. On most datasets, models incorporating the framework achieved F1 scores of more than 99% and average response times of less than 10 μs. In summary, this study provides an effective intelligent solution for the application of real-time receiver spoofing detection.