Authors - Guillermo Francia III, Eman El-Sheikh, Md Abdur Rahman Abstract - Global Navigation Satellite Systems (GNSS) are essential components of modern unmanned aerial vehicle (UAV) operations, providing positioning, navigation, and timing (PNT) services that enable autonomous flight, waypoint navigation, and coordinated mission execution. However, the increasing reliance of UAVs on radio frequency (RF) communications and GNSS signals has exposed these systems to a growing range of cybersecurity threats, including spoofing, jamming, malware infections, distributed denial-of-service (DDoS) attacks, and anomalous network behaviors. This paper investigates the application of deep learning techniques for enhancing RF-based security in GNSS-enabled drone communication networks. A comprehensive drone communication dataset containing 52,585 records and four traffic classes—normal traffic, malware infections, DDoS attacks, and anomalous behavior—was utilized to develop and evaluate a Deep Learning Radio Frequency Security (DL-RFS) model. To address severe class imbalance, a balanced TensorFlow data pipeline incorporating stratified sampling, class-wise dataset generation, and equal-probability sampling was designed. The proposed neural network architecture employs fully connected layers with Rectified Linear Unit (ReLU) activation functions and a softmax output layer optimized using the Adam optimizer. Experimental evaluation conducted on an NVIDIA A100 GPU demonstrated exceptional classification performance, achieving an AUC of 0.999, accuracy of 99.3%, precision of 99.9%, recall of 99.9%, and an F1-score of 1.000. Comparative analysis shows that the proposed DL-RFS model outperforms several state-of-the-art machine learning and deep learning approaches for drone network intrusion detection. The results demonstrate the effectiveness of balanced deep learning pipelines for RF signal security analysis and establish a foundation for future research in GNSS security, RF fingerprinting, and AI-driven cyber defense mechanisms for autonomous systems.
