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.
@article{arxiv.2502.05220,
title = {Aero-LLM: A Distributed Framework for Secure UAV Communication and Intelligent Decision-Making},
author = {Balakrishnan Dharmalingam and Rajdeep Mukherjee and Brett Piggott and Guohuan Feng and Anyi Liu},
journal= {arXiv preprint arXiv:2502.05220},
year = {2025}
}
Comments
This manuscript was accepted by the 1st International Workshop on Integrated Sensing, Communication, and Computing in Internet of Things (IoT) Systems at the The 33rd International Conference on Computer Communications and Networks (ICCCN 2024)