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English(EN) What's on My Network? Using Large Language Models to Identify Real-World IoT Devices at Scale

LLM被用于大规模高精度识别物联网设备

研究人员开发了一种新颖的方法,通过将网络流量元数据视为语言建模任务,以大规模识别物联网(IoT)设备。他们使用大型语言模型构建了一个高保真度的供应商标签数据集,然后在此数据上对LLaMA 3.1 8B模型进行了指令微调。这种方法在设备识别方面取得了高精度,并对数据缺失、协议漂移和对抗性操纵表现出鲁棒性,将LLM定位为可信赖的物联网设备识别的可扩展基础。 AI

影响 这项研究提供了一种可扩展且可解释的物联网设备识别方法,有望提高网络安全和隐私。

排序理由 详细介绍使用LLM进行设备识别新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

LLM被用于大规模高精度识别物联网设备

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详细介绍使用LLM进行设备识别新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Rameen Mahmood, Tousif Ahmed, Sai Teja Peddinti, Danny Yuxing Huang ·

    我的网络上有哪些设备?利用大型语言模型大规模识别真实世界物联网设备

    arXiv:2510.13817v2 Announce Type: replace Abstract: The growth of IoT devices in shared environments has outpaced our ability to identify them, posing urgent risks to privacy, safety, and accountability. This challenge is especially pronounced in open-world environments, where ne…