IoT Device Identification with Machine Learning: Common Pitfalls and Best Practices
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866908793422479360 |
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| author | Kostas, Kahraman Kostas, Rabia Yasa |
| author_facet | Kostas, Kahraman Kostas, Rabia Yasa |
| contents | This paper critically examines the device identification process using machine learning, addressing common pitfalls in existing literature. We analyze the trade-offs between identification methods (unique vs. class based), data heterogeneity, feature extraction challenges, and evaluation metrics. By highlighting specific errors, such as improper data augmentation and misleading session identifiers, we provide a robust guideline for researchers to enhance the reproducibility and generalizability of IoT security models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_20548 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | IoT Device Identification with Machine Learning: Common Pitfalls and Best Practices Kostas, Kahraman Kostas, Rabia Yasa Cryptography and Security Artificial Intelligence Networking and Internet Architecture This paper critically examines the device identification process using machine learning, addressing common pitfalls in existing literature. We analyze the trade-offs between identification methods (unique vs. class based), data heterogeneity, feature extraction challenges, and evaluation metrics. By highlighting specific errors, such as improper data augmentation and misleading session identifiers, we provide a robust guideline for researchers to enhance the reproducibility and generalizability of IoT security models. |
| title | IoT Device Identification with Machine Learning: Common Pitfalls and Best Practices |
| topic | Cryptography and Security Artificial Intelligence Networking and Internet Architecture |
| url | https://arxiv.org/abs/2601.20548 |