CTDNN-Spoof: compact tiny deep learning architecture for detection and multi-label classification of GPS spoofing attacks in small UAVs

Abstract

Abstract GPS spoofing presents a significant threat to small Unmanned Aerial Vehicles (UAVs) by manipulating navigation systems, potentially causing safety risks, privacy violations, and mission disruptions. Effective countermeasures include secure GPS signal authentication, anti-spoofing technologies, and continuous monitoring to detect and respond to such threats. Safeguarding small UAVs from GPS spoofing is crucial for their reliable operation in applications such as surveillance, agriculture, and environmental monitoring. In this paper, we propose a compact, tiny deep learning architecture named CTDNN-Spoof for detecting and multi-label classifying GPS spoofing attacks in small UAVs. The architecture utilizes a sequential neural network with 64 neurons in the input layer (ReLU activation), 32 neurons in the hidden layer (ReLU activation), and 4 neurons in the output layer (linear activation), optimized with the Adam optimizer. We use Mean Squared Error (MSE) loss for regression and accuracy for evaluation. First, early stopping with a patience of 10 epochs is implemented to improve training efficiency and restore the best weights. Furthermore, the model is also trained for 50 epochs, and its performance is assessed using a separate validation set. Additionally, we use two other models to compare with the CTDNN-Spoof in terms of complexity, loss, and accuracy. The proposed CTDNN-Spoof demonstrates varying accuracies across different labels, with the proposed architecture achieving the highest performance and promising time complexity. These results highlight the model’s effectiveness in mitigating GPS spoofing threats in UAVs. This innovative approach provides a scalable, real-time solution to enhance UAV security, surpassing traditional methods in precision and adaptability.

Keywords

• TinyML
• Unmanned Aerial Vehicles (UAVs)
• Spoofing Attacks
• Global Positioning Systems
• Autonomous Vehicle
• Machine Learning