Aero-LLM: A Distributed Framework for Secure UAV Communication and Intelligent Decision-Making
This addresses the need for secure UAV operations in critical applications, representing an incremental improvement through a novel distributed architecture.
The paper tackles the problem of secure and reliable communication for unmanned aerial vehicles (UAVs) by introducing Aero-LLM, a distributed framework that integrates multiple specialized Large Language Models (LLMs) to enhance security and operational efficiency, resulting in outstanding task-specific metrics and robust defense against cyber threats.
Increased utilization of unmanned aerial vehicles (UAVs) in critical operations necessitates secure and reliable communication with Ground Control Stations (GCS). This paper introduces Aero-LLM, a framework integrating multiple Large Language Models (LLMs) to enhance UAV mission security and operational efficiency. Unlike conventional singular LLMs, Aero-LLM leverages multiple specialized LLMs for various tasks, such as inferencing, anomaly detection, and forecasting, deployed across onboard systems, edge, and cloud servers. This dynamic, distributed architecture reduces performance bottleneck and increases security capabilities. Aero-LLM's evaluation demonstrates outstanding task-specific metrics and robust defense against cyber threats, significantly enhancing UAV decision-making and operational capabilities and security resilience against cyber attacks, setting a new standard for secure, intelligent UAV operations.