Cyber-resilient flight architecture for software-defined UAVs using digital twin-based validation
Software-Defined Unmanned Aerial Vehicles (SD-UAVs) rely heavily on software control and networked communication, making them vulnerable to cyber-physical attacks that threaten flight safety and mission reliability. Traditional UAV security approaches are largely reactive and lack integrated resilience mechanisms capable of sustaining stable flight under adversarial conditions. This study proposes a cyber-resilient flight architecture that integrates software-defined control, AI-driven anomaly detection, and Digital Twin-based validation. A Long Short-Term Memory (LSTM) model was implemented for telemetry anomaly detection, while SHAP-based Explainable AI was used to ensure interpretable resilience decisions. The system was implemented using Pixhawk 6X hardware, NVIDIA Jetson Orin Nano processing, MAVLink communication, and a Gazebo Garden + ROS2 Humble simulation environment. A Lyapunov-based stability analysis was conducted to validate the 50 m synchronization loop. Comparative evaluation demonstrated improved anomaly detection latency, reduced false detection rates, faster recovery time, and lower trajectory deviation compared to a conventional UAV architecture. The proposed system maintained stable flight under simulated cyberattack scenarios including command manipulation and sensor perturbation. The integration of Trustworthy AI with digital twin validation enhances UAV resilience, operational safety, and adaptive recovery under cyber threats, providing a scalable framework for secure autonomous flight systems.