用大模型提升物联网的智能响应能力，三类应用效果显著
Integrating Large Language Models with Internet of Things Applications
- 用少样本学习和微调GPT处理物联网异常检测
- 攻击检测准确率从87.6%提升至94.9%，能自动生成应对脚本
- 适合做自然语言交互的物联网系统开发者参考
本文通过三个关键场景案例，分析大语言模型（LLM）在提升物联网（IoT）网络智能化与响应性方面的应用：分布式拒绝服务（DDoS）攻击检测、基于宏观编程的物联网系统控制以及传感器数据处理。结果显示，在少样本学习下，GPT模型的攻击检测准确率达87.6%；经微调后，准确率提升至94.9%。在宏观编程框架下，GPT可利用高层函数生成应对突发事件的脚本。此外，该模型在处理大量传感器数据时，能快速输出高质量结果，包括预期结论与总结洞察。整体表明，大模型具备驱动自然语言接口的潜力。我们希望这些案例能激发研究者进一步探索。
原文摘要 · Abstract (English)
This paper identifies and analyzes applications in which Large Language Models (LLMs) can make Internet of Things (IoT) networks more intelligent and responsive through three case studies from critical topics: DDoS attack detection, macroprogramming over IoT systems, and sensor data processing. Our results reveal that the GPT model under few-shot learning achieves 87.6% detection accuracy, whereas the fine-tuned GPT increases the value to 94.9%. Given a macroprogramming framework, the GPT model is capable of writing scripts using high-level functions from the framework to handle possible incidents. Moreover, the GPT model shows efficacy in processing a vast amount of sensor data by offering fast and high-quality responses, which comprise expected results and summarized insights. Overall, the model demonstrates its potential to power a natural language interface. We hope that researchers will find these case studies inspiring to develop further.
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