An Interpretable Ensemble Learning Method for GPS Spoofing Detection with Feature Selection

Global Positioning System (GPS) spoofing poses severe threats to navigation safety, necessitating robust detection mechanisms with enhanced interpretability. This study proposes Stack-TabNet, a novel stacked ensemble learning framework integrating XGBoost, Random Forest, and the attentive transformer-based TabNet network. To address model opacity, an interpretable feature attribution mechanism is employed to quantify feature contributions and guide optimization. Experiments are conducted on a complex dataset comprising authentic and spoofed GPS signals across four classes, characterized by high-dimensional signal metrics and severe class imbalance. The initial model utilizing all available features demonstrates robust detection capability. Subsequently, an optimized variant utilizes a subset of top-ranked features identified by the interpretation mechanism, yielding further improved accuracy. Comparative analysis confirms that the proposed framework surpasses all traditional machine learning and deep learning baselines. The analysis identifies Pseudorange and Time of Code Delay as the most discriminative features. These results indicate that combining ensemble learning with interpretable feature selection significantly enhances detection accuracy and training efficiency for GPS anti-spoofing applications.