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LLMs used to identify IoT devices at scale with high accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel method for identifying Internet of Things (IoT) devices at scale by treating network traffic metadata as a language modeling task. They constructed a high-fidelity dataset of vendor labels using large language models and then instruction-tuned a LLaMA 3.1 8B model on this data. This approach achieved high accuracy in device identification, demonstrating robustness against missing data, protocol drift, and adversarial manipulation, positioning LLMs as a scalable foundation for trustworthy IoT device identification. AI

IMPACT This research offers a scalable and interpretable method for identifying IoT devices, potentially improving network security and privacy.

RANK_REASON Academic paper detailing a new methodology for device identification using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs used to identify IoT devices at scale with high accuracy

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Academic paper detailing a new methodology for device identification using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

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

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

    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…

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