The GNSS Spoofing Detection Method Based on AdaBoost

GNSS is widely used in various aspects of modern life, but it remains vulnerable to spoofing attacks. As a result, detecting GNSS spoofing attacks quickly and accurately has become an important research topic. Traditional spoofing detection rely on detecting changes in single-parameter features, such as signal power and signal quality, which lack adaptability. With the development of modern artificial intelligence theories, more applicable solutions for spoofing detection have emerged. A GNSS spoofing detection algorithm based on AdaBoost is proposed in the paper. Firstly, a software receiver is used to extract eight detection features, including SQM, C/N 0 , pseudo-range-Doppler consistency, pseudo-range and Doppler solution residual, and clock drift, and then the AdaBoost model is used for training and testing. The proposed method achieves highly accurate detection rates of 97% and 98% on the two public datasets, TEXBAT and OAKBAT, respectively. Moreover, it outperforms two common machine learning methods with higher detection accuracy and stable operational efficiency.