###### Abstract  
GPS spoofing attacks are a severe threat to unmanned aerial vehicles. These attacks manipulate the true state of the unmanned aerial vehicles, potentially misleading the system without raising alarms. Several techniques, including machine learning, have been proposed to detect these attacks. Most of the studies applied machine learning models without identifying the best hyperparameters, using feature selection and importance techniques, and ensuring that the used dataset is unbiased and balanced. However, no current studies have discussed the impact of model parameters and dataset characteristics on the performance of machine learning models; therefore, this paper fills this gap by evaluating the impact of hyperparameters, regularization parameters, dataset size, correlated features, and imbalanced datasets on the performance of six most commonly known machine learning techniques. These models are Classification and Regression Decision Tree, Artificial Neural Network, Random Forest, Logistic Regression, Gaussian Naive Bayes, and Support Vector Machine. Thirteen features extracted from legitimate and simulated GPS attack signals are used to perform this investigation. The evaluation was performed in terms of four metrics: accuracy, probability of misdetection, probability of false alarm, and probability of detection. The results indicate that hyperparameters, regularization parameters, correlated features, dataset size, and imbalanced datasets adversely affect a machine learning model's performance. The results also show that the Classification and Regression Decision Tree classifier has an accuracy of 99.99%, a probability of detection of 99.98%, a probability of misdetection of 0.2%, and a probability of false alarm of 1.005%, after removing correlated features and using tuned parameters in a balanced dataset. Random Forest can achieve an accuracy of 99.94%, a probability of detection of 99.6%, a probability of misdetection of 0.4%, and a probability of false alarm of 1.01% in similar conditions. unmanned aerial vehicle; GPS spoofing attacks; machine learning; dataset bias; hyperparameter tuning; dataset imbalance; dataset size; correlated features; regularized learning parameters +
Footnote †: journal: Medical Engineering and Computer Science  
Numerous studies have been conducted to detect, identify, and mitigate attacks on UAVs. Table 1 compares existing machine learning techniques with respect to the dataset and model parameters. The authors of [6] proposed a technique for classifying GPS spoofing attacks using artificial neural networks (ANNs) with a benchmark that includes signal-to-noise ratio, pseudo-range, and Doppler shift. The authors of [7] proposed using Linear Regression and Long Short-Term Memory to detect GPS spoofing attacks and evaluated their proposed model using time steps and neurons. The authors of [8] used the least absolute shrinkage and selector operator to detect and classify GPS spoofing attacks on UAVs. The authors of [9] proposed a k-learning-based approach and evaluated its performance in terms of several k values. An algorithm based on Support Vector Machine (SVM) was proposed by the authors of [10], who evaluated its performance using the evaluation window and time width. The authors of [11] proposed a technique, Long Short-Term Memory, using a dataset of features such as flight speed, altitude, and range.