An Adaptive Framework for Collective Anomaly Detection in Key Performance Indicators From Mobile Networks

Abstract

Anomaly detection is a critical component of Self-Organizing Networks (SON), enhancing network efficiency and resilience. This paper proposes a novel framework for detecting collective anomalies in univariate Key Performance Indicators (KPIs) in mobile networks. By leveraging data mining and Machine Learning (ML) techniques, the framework enables timely anomaly detection without requiring expert-labeled data. The proposed framework starts by clustering time series from 11 distinct KPIs into four groups. Then, representative KPIs from each cluster are selected to evaluate the anomaly detection performance using two algorithms: the Smart Trouble Ticket Management (STTM) and STUMPY. The STTM algorithm is applied to KPIs with low variability, such as Call Setup Success Rate and Service Drop Rate, showing high accuracy in anomaly detection. For KPIs with higher variability, such as User Downlink (DL) Average Throughput and DL Resource Block Utilization Rate, the STUMPY algorithm is employed, yielding similarly accurate results. The framework, composed of the STTM and the STUMPY algorithms, demonstrates effective anomaly detection, achieving high precision (0.94), recall (0.86), and an F1-score of 0.90, with minimal False Positives (FP). These results underline the framework’s reliability across different types of KPIs, providing a robust solution for anomaly detection in mobile network monitoring, outperforming benchmark algorithms in all key metrics.