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

Giovanni Trappolini, Antonio Purificato, Federico Siciliano, Luigi D'Addona, Anna Maria Spagnolo, Domenico Dato, Fabrizio Silvestri

Abstract: With the arrival of 5G technology, networks face critical challenges in detecting anomalies that can significantly impact performance and reliability. This paper introduces QAED (Quantized Auto Encoder Detector), a novel deep learning approach for anomaly detection in 5G networks with three key innovations: 1) a vector quantization mechanism that effectively captures discrete network states, 2) a kernel density estimation preprocessing step that enables detection of both outliers and distribution shifts, and 3) an integrated architecture that processes multivariate time series data in a unified framework. We provide a detailed evaluation of our model across 5G data scenarios, demonstrating its enhanced accuracy and efficiency in anomaly detection compared to existing state-of-the-art methods, with gains of up to 8%.

External IDs:dblp:journals/access/TrappoliniPSDSDS25