Quantized Auto Encoder-Based Anomaly Detection for Multivariate Time Series Data in 5G Networks

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...

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Bibliographic Details
Main Authors: Giovanni Trappolini, Antonio Purificato, Federico Siciliano, Luigi D'Addona, Anna Maria Spagnolo, Domenico Dato, Fabrizio Silvestri
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
5G
anomaly detection
deep learning
Online Access:https://ieeexplore.ieee.org/document/10992680/
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https://ieeexplore.ieee.org/document/10992680/

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