用大模型统一管理物联网设备并检测异常流量，效果领先。
A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection
- 结合上下文的大模型处理设备管理任务，提升响应准确性。
- 异常检测模块在真实数据上达到当前最优性能。
- 适合需要高效运维和安全防护的物联网系统开发者。
物联网生态系统的快速扩展带来了设备管理与网络安全的日益复杂挑战。为此，我们提出一个统一框架，将基于上下文的大型语言模型（LLMs）用于物联网管理任务，并集成微调后的异常检测模块进行网络流量分析。该框架通过利用物联网手册和运营数据中的上下文知识，简化设备管理、故障排查与安全策略执行等流程。异常检测模型在识别物联网流量中的异常行为与威胁方面表现卓越，经微调后实现了优异的准确率。评估表明，引入相关上下文信息可显著提升基于LLM的响应精度与可靠性。同时，执行时间、内存消耗及响应效率等资源使用指标显示该框架具备良好的可扩展性，适用于真实世界物联网部署。
原文摘要 · Abstract (English)
The rapid expansion of Internet of Things (IoT) ecosystems has introduced growing complexities in device management and network security. To address these challenges, we present a unified framework that combines context-driven large language models (LLMs) for IoT administrative tasks with a fine-tuned anomaly detection module for network traffic analysis. The framework streamlines administrative processes such as device management, troubleshooting, and security enforcement by harnessing contextual knowledge from IoT manuals and operational data. The anomaly detection model achieves state-of-the-art performance in identifying irregularities and threats within IoT traffic, leveraging fine-tuning to deliver exceptional accuracy. Evaluations demonstrate that incorporating relevant contextual information significantly enhances the precision and reliability of LLM-based responses for diverse IoT administrative tasks. Additionally, resource usage metrics such as execution time, memory consumption, and response efficiency demonstrate the framework's scalability and suitability for real-world IoT deployments.
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