Quantized Auto Encoder-Based Anomaly Detection for Multivariate Time Series Data in 5G Networks
With the arrival of 5G technology, networks face critical challenges in detecting anomalies that can significantly impact performance and reliability. This paper introduces <monospace>QAED</monospace> (Quantized Auto Encoder Detector), a novel deep learning approach for anomaly detection in 5G netwo...
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| Auteurs principaux: | , , , , , , |
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| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
IEEE
2025-01-01
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| Collection: | IEEE Access |
| Sujets: | |
| Accès en ligne: | https://ieeexplore.ieee.org/document/10992680/ |
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