A Comparative Assessment of Unsupervised Deep Learning Models for Detecting GPS Spoofing Attacks on Unmanned Aerial Systems

Unmanned Aerial Vehicles (UAV) 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.