A Deep Learning Based Code Loop Discriminator for GPS Spoofing Mitigation

A vital part of people's daily life is the position, navigation, and time service provided by the Global Positioning System (GPS), which is always accessible globally. Consequently, the security of the GPS receivers is crucial. Occasionally, intentional and unintentional interferences cause GPS location issues. Spoofing attack is the most severe interference to the GPS receivers, which results in positional mistakes. This paper's goal is to defend against the carry-off spoofing attacks. In a carry-off spoofing attempt, the spoofer transmits signals whose code phase and carrier frequency parameters are strikingly close to the actual signal in order to change the correlation values generated in the tracking stage. Discriminator output values alter as correlation values change. As a result, the Pseudo Random Noise (PRN) code generator unit creates a local replica, which forces the tracking loop to follow the fake signal instead of the real one. It is proposed in this paper that when spoofing attacks occur, discriminator output values be generated independently of correlation values. Specifically, when a spoofing signal is detected, the conventional discriminator is replaced by a Non-linear Autoregressive Exogenous Neural Network (NARX NN)-based predictor. This strategy protects the tracking loop from the effects of the spoofing signal. The efficiency of the provided strategy was evaluated using three spoofing data sets. The results of the suggested mitigation method, based on NARAX NN, show that it mitigates spoofing attacks by an average of 95.82%.