###### Abstract
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Detection Model**} & \multicolumn{3}{c}{**Dataset Characteristics**} & \multicolumn{3}{c}{**Model Parameters**} \\ \cline{2-6}  & **Correlation** & **Class Imbalance** & **Dataset Size** & **Hyperparameter Tuning** & **Regularization** \\ \hline Artificial Neural & - & Balanced & 3000 Samples & - & - \\ Network [6] & - & & & & \\ \hline Linear Regression-Based & & & & & \\ and Long Short-Term & - & - & 40,000 Samples & - & - \\ Memory [7] & & & & & \\ \hline Selection Operator, Least & Two-Phase & & - & Selection Operator and Least Absolute & - \\ Absolute Shrinkage [8] & Correlator & - & - & Shrinkage & - \\ \hline K-Learning-Based [9] & - & - & - & - & - \\ \hline Support Vector & & - & - & - & - \\ Machine [10] & - & - & - & - & - \\ \hline Long Short-Term & & & & & \\ Memory [11] & - & Balanced & - & - & - \\ \hline Support Vector & Pearson’s & & & & \\ Machine [12] & Correlation & - & - & - & - \\ \hline DeepSIM [13] & - & - & 7740 Images & - & - \\ \hline Metric Optimized & & & & & \\ Dynamic and Weighted & & & & & \\ Metric Optimized & & & & & \\ Dynamic [14] & & & & & \\ \hline Random Forest, & & & & & \\ Gradient Boost, & & & & \\ XGBoost, and & & & & & \\ LightGBM [2] & & & & & \\ \hline Bagging, Boosting, and & & & & & \\ Stacking [15] & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of existing machine learning techniques with respect to dataset characteristics and model parameters. The authors of [12] also applied SVM to detect GPS spoofing attacks by conducting a correlation analysis and evaluating their model based on accuracy. The authors of [13] used a deep learning (DL)-based method, DeepSIM, to detect GPS spoofing attacks by employing a camera and comparing historical GPS images to incoming GPS images using image processing techniques. The authors of [14] proposed two dynamic selection approaches based on ten commonly used ML models. The authors of [2] compared several tree-based ML models, Extreme Gradient Boosting (XGBoost), Random Forest (RF), Gradient Boosting (GBM), and Light Gradient Boosting (LightGBM), to detect GPS spoofing attacks targeting UAVs. The authors performed a correlation analysis and used a benchmark with 13 features. The authors of [15] analyzed three types of ensemble models to detect GPS spoofing attacks targeting UAVs, including bagging, boosting, and stacking. The authors of [16] also compared the performance of supervised and unsupervised ML models, namely Gaussian Naive Bayes, Random Forest, Classification and Regression Decision Tree, Linear-Support Vector Machine, Logistic Regression, Principal Component Analysis, Artificial Neural Network, and Autoencoder, to detect GPS spoofing attacks. Another study [17] incorporated Support Vector Machine with K-fold cross-validation to detect GPS spoofing attacks on UAVs. The authors of [18] proposed a semantic-based detection technique, CONSDET, to support onboard GPS spoofing attack detection. The authors of [19] proposed a resilient state estimation framework that combines Kalman filter and Inertial Measurement Unit to address UAV sensor drift issues. The authors of [20] provided a strategy, 5G-assisted UAV position monitoring, and an anti-GPS spoofing system to detect live GPS spoofing attack detection. This strategy involves the uplink receiving signal strength measurements used to detect these attacks. The authors of [21] used a vision-based approach, combining UAV's sensors, camera, and Inertial Measurement Unit to detect GPS spoofing attacks on small UAVs. Another study [22] used a vision-based approach, employing Visual Odometry methods to detect GPS spoofing attacks on UAVs. The authors compared the extracted images with the flight trajectory information to detect and classify spoofed signals.