LLM-LCSA: LLM for Collaborative Control and Decision Optimization in UAV Cluster Security

Abstract

Highlights: What are the main findings? The proposed LLM-LCSA architecture improves threat detection accuracy by an average of 7.92% and reduces total system response time by 44.52% compared to traditional methods. A Mixture of Experts (MoEs) mechanism incorporating a dynamic threat–expert association matrix enables adaptive and real-time identification of complex threats. What is the implication of the main finding? The cloud–edge–end hierarchical framework provides a scalable and efficient architecture for secure, intelligent collaboration in large-scale UAV swarms. The resource-aware multi-objective decision model ensures reliable performance under stringent resource constraints, enhancing practicality for real-world deployments. With the development of unmanned aerial vehicle (UAV) technology, multimachine collaborative operations have become the core model for increasing mission effectiveness. However, large-scale UAV clusters face challenges such as dynamic security threats, heterogeneous data fusion difficulties, and resource-constrained decision-making delays. Traditional single-machine intelligent architectures have limitations when addressing new threats, such as insufficient real-time response capabilities. To address these issues, this paper presnts an LLM-layered collaborative security architecture (LLM-LCSA) for multimachine collaborative security. This architecture optimizes the spatiotemporal fusion efficiency of multisource asynchronous data through cloud–edge–end collaborative deployment, combining an end lightweight LLM, an edge medium LLM, and a cloud-based foundation LLM. Additionally, a Mixture of Experts (MoEs) intelligent algorithm that dynamically activates the most relevant expert models by leveraging a threat–expert association matrix is introduced, thereby increasing the accuracy of complex threat identification and dynamic adaptability. Moreover, a resource-aware multi-objective optimization model is constructed to generate optimal decisions under resource constraints. Simulation results indicate that compared with traditional methods, LLM-LCSA achieves an average 7.92% improvement in the threat detection accuracy, reduces the system’s total response time by 44.52%, and enables resource scheduling during off-peak periods. This architecture provides an efficient, intelligent, and scalable solution for secure collaboration among UAV swarms. Future research should further explore its application potential in 6G network integration and large-scale swarm environments.