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Unmanned aerial vehicles (UAVs) are prone to several cyber-attacks, including global positioning system (GPS) spoofing. The use of machine learning and deep learning are becoming increasingly common for UAV GPS spoofing attack detection; however, these approaches have some limitations, such as a high rate of false alarm and misdetection. We propose using capsule networks to detect and classify UAV-focused GPS spoofing attacks. This paper compares simple capsule networks, efficient capsule networks, dual attention capsule networks, and convolutional neural network in terms of accuracy, probability of detection, probability of misdetection, probability of false alarm, prediction time, training time per sample, and memory size. The results indicate that the Efficient-capsule network outperforms the other models, as demonstrated by an accuracy of 99.1%, a probability of detection of 99.9%, a probability of misdetection of 0.1%, a probability of false alarm of 0.37%, a prediction time of 0.5 seconds, a training time per sample of 0.2 seconds, and a memory size of 123 mebibytes for binary classification. 

Unmanned Aerial Vehicles (UAVs) are prone to cyber threats, including Global Positioning System (GPS) spoofing attacks. Several studies have been performed to detect and classify these attacks using machine learning and deep learning techniques. Although these studies provide satisfactory results, they deal with several limitations, including limited data samples, high costs of data annotations, and investigation of data patterns. Unsupervised learning models can address these limitations. Therefore, this paper compares the performance of four unsupervised deep learning models, namely Convolutional Auto Encoder, Convolutional Restricted Boltzmann Machine, Deep Belief Neural Network, and Adversarial Neural Network in detecting GPS spoofing attacks on UAVs. The performance evaluation of these models was done in terms of Gap static, Calinski harabasz score, Silhouette Score, homogeneity, completeness, and V-measure. The results show that the Convolutional Auto-Encoder has the best performance results among the other unsupervised deep learning models. 

Unmanned Aerial Networks (UAVs) are prone to several cyber-attacks, including Global Positioning Spoofing attacks. For this purpose, numerous studies have been conducted to detect, classify, and mitigate these attacks, using Artificial Intelligence techniques; however, most of these studies provided techniques with low detection, high misdetection, and high bias rates. To fill this gap, in this paper, we propose three supervised deep learning techniques, namely Deep Neural Network, U Neural Network, and Long Short Term Memory. These models are evaluated in terms of Accuracy, Detection Rate, Misdetection Rate, False Alarm Rate, Training Time per Sample, Prediction Time, and Memory Size. The simulation results indicated that the U Neural Network outperforms other models with an accuracy of 98.80%, a probability of detection of 98.85%, a misdetection of 1.15%, a false alarm of 1.8%, a training time per sample of 0.22 seconds, a prediction time of 0.2 seconds, and a memory size of 199.87 MiB. In addition, these results depicted that the Long Short-Term Memory model provides the lowest performance among other models for detecting these attacks on UAVs.