用联邦学习+多模态大模型实现分布式安全检测，兼顾隐私与精度。
Design and implementation of a distributed security threat detection system integrating federated learning and multimodal LLM
- 联邦学习保护数据隐私，多模态大模型融合多种异构数据。
- 检测准确率达96.4%，误报率和漏报率分别降低1.8%和2.4%。
- 适合大规模分布式系统部署，训练仅需180秒，检测3.8秒完成。
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，尤其在检测精度与数据隐私之间难以平衡。本文提出一种新型分布式安全威胁检测系统，融合联邦学习与多模态大语言模型（LLMs）。系统通过联邦学习保障数据隐私，利用多模态LLMs处理网络流量、系统日志、图像及传感器数据等异构数据源。在10TB分布式数据集上的实验表明，该方法检测准确率达到96.4%，较传统基线模型提升4.1个百分点；同时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，系统在分布式环境中具备高效处理能力，模型训练耗时180秒，跨网络威胁检测仅需3.8秒。结果表明，该方案在检测精度与计算效率方面均有显著提升，同时有效保护数据隐私，具备较强的实际部署潜力。
原文摘要 · Abstract (English)
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.
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