# Detecting and Mitigating Attacks on GPS Devices
## 5 Detection Techniques and Their Comparison
### GPS Spoofing Detection  
GPS spoofing has been a recent topic of intense interest; therefore, there are many published papers on the subject. There are three primary approaches for GPS spoofing detection: ML-based, antenna/direction-of-arrival (DOA)-based, and movement tracking-based (Table 10). Most of the proposed methods for GPS spoofing detection are based on machine learning approaches. These approaches share a largely common workflow: (1) conduct a set of experiments for which the outputs are known; (2) measure a set of observable parameters that have been deemed essential; (3) compile those measured values along with the known outputs into a training dataset; (4) train the machine learning model; (5) evaluate the performance of that machine learning model with a separate dataset of measured parameters and known outputs; and (6) compare the outputs predicted by the machine learning model with the known outputs. The key differences between the proposed approaches are (1) features chosen for inclusion in model training, (2) model selection, (3) chosen performance metrics, and (4) achieved performance. Table 11 summarizes some of the proposed ML-based approaches. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of Approach** & **General Idea** & **Strengths** & **Key Open Research Questions** \\ \hline ML-based & \begin{tabular}{c} Use measurable features of GPS \\ signal, spoofed signal, and GPS \\ receiver to train an ML model for \\ future predictions on future data \\ based on those same features. \\ \end{tabular} & \begin{tabular}{c} Based on easily observable \\ parameters. \\ Demonstrated good performance. \\ \end{tabular} & \begin{tabular}{c} Can ML models be sufficiently \\ optimized to run on small \\ platforms with limited \\ computational capability? \\ \end{tabular} \\ \hline Antenna/DOA-based & \begin{tabular}{c} Utilize antenna array to measure \\ aspects of signal to discern \\ between authentic signals and \\ spoofed signals. \\ \end{tabular} & \begin{tabular}{c} Based on easily observable \\ parameters, few computational \\ requirements. \\ Demonstrated good performance. \\ \end{tabular} & \begin{tabular}{c} Can antenna arrays be made \\ sufficiently simple to be viable for \\ small platforms? \\ \end{tabular} \\ \hline Movement tracking-based & \begin{tabular}{c} Use the movement history of the \\ platform to identify anomalies \\ and outliers in position estimates. \\ \end{tabular} & \
\begin{tabular}{c} Simple to implement, few \\ computational requirements. \\ Demonstrated good performance. \\ \end{tabular} &
\begin{tabular}{c} How will these approaches work \\ for complex flight paths? \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Summary of most common approaches in the literature to GPS spoofing detection. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  
For instance, the authors of [84] proposed an SVM-based approach to GPS spoofing detection, where they looked at the difference between position as derived from GPS and the onboard inertial sensors and then determined if the difference was due to spoofing or inertial errors through an SVM model. The authors of [85] presented a linear regression approach to GPS spoofing detection. In this approach, the UAS flight trajectory prediction model was obtained by fitting the UAS's flight log with the linear regression model. The authors of [86] proposed using fuzzy logic in the signal acquisition process. The proposed approach aligns the value of the acquisition threshold using parameters affecting acquisition performance in the presence of the spoofed GPS signal. The ratio between the correlation levels was then used to distinguish between the actual and spoofed GPS signals. The authors of [87] proposed a game-theoretic approach to GPS spoofing detection for UAS applications. The proposed approach viewed the interactions between a GPS spoofer and UAS operator as a Stackelberg game, showing this approach outperformed other game strategies.