Enhancing Network Performance Monitoring through Scalable Multi-Dimensional Metric Analysis and Pattern-Based Anomaly Detection

This paper presents a novel approach to network performance monitoring and improvement through profile pattern extraction-based anomaly detection in multi-dimensional network throughput metrics. As modern networks grow in complexity, traditional monitoring methods often struggle to detect subtle yet significant anomalies that can impact performance. Our research addresses this challenge by developing an integrated framework that combines advanced data analysis techniques with machine learning algorithms to identify and interpret complex patterns in network behaviour. The proposed methodology leverages autocorrelation function (ACF) based clustering to group similar time series, and employs feature extraction methods to create profile patterns from multi-dimensional network data. These patterns serve as a baseline for normal network behaviour, against which anomalies are detected using the Isolation Forest algorithm. These patterns serve as a baseline for normal network behaviour, against which anomalies are detected using a combination of statistical methods and machine learning approaches. Our experimental results, based on real-world data from a telecommunications network, demonstrate that the profile pattern-based approach significantly enhances anomaly detection capabilities. The best-performing model, which combines raw data and Z-scores derived from profile patterns, achieved an anomaly detection rate of 1.805% with the highest confidence (average anomaly score of -0.123). This model outperformed both raw data analysis and Z-score-only approaches in terms of selectivity and computational efficiency, completing analysis in 8.582 seconds. This research contributes to the field of network performance monitoring by offering a more sophisticated and accurate approach to anomaly detection, potentially leading to enhanced network reliability, reduced downtime, and improved user experience. The paper concludes by discussing the implications of these findings for network administrators and outlining future research directions in this rapidly evolving field.