This content delves into the complexities of labeling IoT devices using innovative AI solutions. The authors propose a method that passively monitors network traffic to automatically label unseen devices based on vendors and functions. By combining Large Language Models (LLMs) with real-time identification techniques, they achieve significant accuracy in function labeling for previously unseen IoT devices.
The study highlights the importance of accurate device labeling for effective network management and security in the rapidly growing IoT landscape. It introduces a unique approach that surpasses existing methods, such as Fing, by achieving higher accuracy rates through advanced algorithms like Roberta.
Through detailed experiments and comparisons with other common methods, the authors demonstrate the effectiveness of their proposed solution in accurately identifying unknown IoT devices. They emphasize the significance of explainability, passive operation, and offline processing in their labeling algorithm.
Overall, this research contributes valuable insights into enhancing IoT security through automated device labeling using cutting-edge technologies like LLMs and NLP.
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arxiv.org
Önemli Bilgiler Şuradan Elde Edildi
by Bar Meyuhas,... : arxiv.org 03-05-2024
https://arxiv.org/pdf/2403.01586.pdfDaha Derin Sorular