Leveraging Machine Learning for Anomaly Detection in Telecom Network Management

Abstract

Telecommunications networks form critical infrastructure requiring exceptional reliability amidst growing complexity. Traditional monitoring approaches based on static thresholds increasingly fall short as 5G deployments, software-defined networking, and network function virtualization create dynamic environments generating massive operational data volumes. Machine learning offers transformative capabilities for anomaly detection in these networks, enabling proactive identification of potential failures before service disruption occurs. This article explores how artificial intelligence techniques, including supervised learning, unsupervised learning, and time series analysis, can be applied to telecom network management, highlighting architectural frameworks and real-world applications such as performance monitoring, predictive maintenance, security threat detection, and root cause analysis. While implementation challenges persist around data quality, model explainability, legacy system integration, and ethical considerations, emerging technologies like federated learning, reinforcement learning, and digital twins promise to further enhance network intelligence while addressing current limitations.