GP-adapted attacks pose a significant risk to modern navigation and positioning systems and can introduce security vulnerabilities into a wide variety of applications, including financial services, transportation, and defense. This paper proposes the first reliable autolearning-based approach to route GPS attack detection based on traffic type analysis. The proposed methodology applies supervised learning techniques such as Random Forest, XGBoost, and Long Short-Term Memory (LSTM) combined with feature selection and data preprocessing. Owing to the distinct properties of the dataset, to optimally train the model, multiple parameters of the network traffic were studied and optimised. To enhance the model interpretability, we apply explanatory approaches such as shap and lime to estimate the importance of the signals. Evaluation metrics like accuracy, recall, precision, and F1 show how the proposed model proved to be efficient, where XgBoost has ranked the highest. They also perform competitive tests to benchmark the stability model against complex attack scenarios. The research not only advances the domain of cybersecurity, providing an evolutionary and interpretative framework for automatic learning for detecting real-time GPS counterfeiting. The findings underscore the potential of cyberrose-managed safety solutions guaranteeing the reliability of positional technologies. To enhance security stability, further research will combine deep training methods with a real-world deployment approach.