###### 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 Technique** & **Class Imbalance** & **Dataset Size** & **Hyperparameter Tuning** & **Regularization** \\ \hline Gaussian Naive Bayes, & & & & & \\ Random Forest, & & & & & \\ Classification and & & & & & \\ Regression Decision & & & & & \\ Tree, Linear-Support & Pearson’s & & & Grid Search and & \\ Vector Machine, Logistic & Correlation & Balanced & 10,055 Samples & Adadelta & - \\ Regression, Principal & Coefficient & & & optimizer & \\ Component Analysis, & & & & & \\ Artificial Neural & & & & & \\ Network, and & & & & & \\ Autoencoder [16] & & & & & \\ \hline Support Vector Machine & & & & & K-fold cross & - \\ and K-fold [17] & - & - & - & validation & - \\ \hline CONSDET [18] & Simple Correlation & Balanced & 10,296 Samples & - & - \\ \hline Resilient State & & & & & \\ Estimation [19] & - & - & - & - & - \\ \hline
5G-assisted UAV [20] & - & - & - & - & - \\ \hline Vision Inertial & & & & & \\ Measurement Unit [21] & - & - & - & - & - \\ \hline Visual Odometry [22] & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Most research on this subject has focused on using specific ML and DL models without addressing the impact of dataset characteristics and model parameters on model performance (Table 1). Multiple issues can hinder the development of feasible models for the problem at hand. For instance, classification algorithms can perform poorly and have low generalization ability when trained on small or biased datasets. The classification problem becomes more challenging when working with unreliable and biased data, such as datasets containing correlated features. Data reflecting GPS spoofing attacks are challenging to acquire and are limited; therefore, it is essential to find solutions that improve the quality of the corresponding dataset instead of focusing solely on increasing model accuracy. Investigating the impact of these factors on GPS spoofing detection techniques is needed to provide a consensus on best practices and create a basis for future research directions. In this work, we investigate the key factors that impact the performance of the most widely used AI models, including dataset characteristics and model parameters such as feature correlation, class imbalance, dataset size, hyperparameter tuning, and regularization (Figure 1). The investigated models in this study are SVM, ANN, RF, Gaussian Naive Bayes (GNB), Classification, Regression Decision Tree (CART), and Logistic Regression (LR). We used a benchmark consisting of 13 features [15] for training and testing the models. The performance of the models are evaluated on the basis of accuracy (\(ACC\)), probability of false alarm (_Pfu_), probability of detection (_PD_), and probability of misdetection (_Pmd_). The main contributions of this paper are:  
* Evaluating the impact of hyperparameter tuning and regularization parameters on the performance of ML techniques to detect GPS spoofing attacks targeting UAVs,
* Evaluating the impact of correlated and uncorrelated features on ML model performance,
* Investigating the impact of correlated features on ML model performance with respect to dataset size,
* Examining the impact of the percentage of malicious samples in the dataset on ML model performance. The remainder of this paper is organized as follows: Section 2 indicates the used materials and methodology, Section 3 presents and discusses the simulation results, and the conclusion and future work recommendations are highlighted in Section 4.