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

Summary

Researchers propose an RL-based routing scheme for UAVs that uses an LLM to infer environmental and jamming features from multimodal sensing data, along with neighbor location and speed, to choose next hops and relay power. Trust management built from packet forwarding and environmental features selects higher-trust next hops and enables self-healing against gray-hole attacks, while penalizing decisions that trigger frequent path reconstructions. Experiments with a 7-billion-parameter LLaVA model at the control center show performance gains against smart jamming and gray-hole attacks.

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