Global positioning system spoofing detection based on Support Vector Machines - Xuefen Zhu; Teng Hua; Fan Yang; Gangyi Tu; Xiyuan Chen (IET Radar, Sonar & Navigation, 2022)
Abstract The civil Global Positioning System (GPS) is vulnerable to spoofing because of its open signal structure. The performance of previous spoofing detection methods is often limited due to spoofing's strong concealment. In this study, a method is proposed to detect spoofing by analysing the features of improved signal quality monitoring (SQM) moving variance (MV), improved SQM moving average (MA), early‐late phase, carrier‐to‐noise ratio–MV and clock offset rate of receiver using Support Vector Machines. Then, the effectiveness of different kernel functions is compared along with other previous methods, revealing that our method outperforms previous methods when coarse Gaussian is used as kernel function. Specifically, the f1 score of the proposed method is improved by 3.22%, 12.85% and 35.72% in comparison with Back Propagation network, Ratio and Delta. The authors hope this work is beneficial for future research and for the implementation of GPS spoofing detection technology and high‐performance receiver, which is of great significance to maintain the normal operation of GPS.