Robust unsupervised outlier detection in IoT using contrastive learning-driven autoencoders
Abstract Outlier detection plays a critical role in ensuring the reliability, security, and operational stability of Internet of Things (IoT) systems, where data streams are often high-dimensional, heterogeneous, and unlabeled. In this paper, we propose a robust unsupervised outlier detection framew...
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| Format: | Artigo |
| Sprache: | Inglês |
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Nature Portfolio
2026-05-01
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| Schriftenreihe: | Scientific Reports |
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| Online-Zugang: | https://doi.org/10.1038/s41598-026-51397-y |
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