Learning-Based LLM-Enhanced UAV Routing Against Jamming and Gray-Hole Attacks

This paper proposes a routing system for uncrewed aerial vehicles (UAVs, or drones) that combines reinforcement learning (RL, a type of AI that learns by trial and error) with large language models (LLMs) to help drones find reliable communication paths when facing jamming attacks (deliberate radio interference) and gray-hole attacks (where nodes pretend to forward data but secretly drop it). The system uses sensor data like images and temperature readings to predict jamming and avoid compromised routes, resulting in better packet delivery and lower delays.