Traditional security protection methods struggle to address sophisticated attack vectors in large-scale distributed systems, particularly when balancing detection accuracy with data privacy concerns. This paper presents a novel distributed security threat detection system that integrates federated learning with multimodal large language models (LLMs). Our system leverages federated learning to ensure data privacy while employing multimodal LLMs to process heterogeneous data sources including network traffic, system logs, images, and sensor data. Experimental evaluation on a 10TB distributed dataset demonstrates that our approach achieves 96.4% detection accuracy, outperforming traditional baseline models by 4.1 percentage points. The system reduces both false positive and false negative rates by 1.8 and 2.4 percentage points respectively. Performance analysis shows that our system maintains efficient processing capabilities in distributed environments, requiring 180 seconds for model training and 3.8 seconds for threat detection across the distributed network. These results demonstrate significant improvements in detection accuracy and computational efficiency while preserving data privacy, suggesting strong potential for real-world deployment in large-scale security systems.

翻译：传统安全防护方法难以应对大规模分布式系统中的复杂攻击向量，特别是在平衡检测精度与数据隐私保护方面存在挑战。本文提出一种集成联邦学习与多模态大语言模型（LLM）的新型分布式安全威胁检测系统。该系统利用联邦学习确保数据隐私，同时采用多模态LLM处理包括网络流量、系统日志、图像和传感器数据在内的异构数据源。在10TB分布式数据集上的实验评估表明，我们的方法实现了96.4%的检测准确率，较传统基线模型提升4.1个百分点。该系统将误报率和漏报率分别降低1.8和2.4个百分点。性能分析显示，我们的系统在分布式环境中保持高效处理能力，模型训练耗时180秒，分布式网络威胁检测仅需3.8秒。这些结果证明了该系统在保护数据隐私的同时，显著提升了检测精度与计算效率，表明其在大规模安全系统中具有实际部署的强健潜力。