Big Data Analytics for Cross-Domain Anomaly Detection to Identify Hidden Service-Impacting Patterns in Fiber Access Broadband Networks

Abstract

-Fibre-to-the-x networks are observed through several operational systems, yet service-impacting degradation often remains hidden because each system describes only a fragment of the end-to-end service. Optical power, line errors, dynamic bandwidth allocation, IP-session behaviour, customer-premises telemetry, topology, alarms and complaints may each remain within local thresholds while their joint movement signals a developing fault. This review examines how cross-domain data correlation and anomaly detection can expose such weak, distributed evidence before it becomes a widespread outage. It synthesises research published from 2020 to 2025 on multivariate time-series modelling, graph learning, feature-domain interaction, optical-network failure management and explainable detection. The review proposes an evidence-centred architecture that aligns heterogeneous telemetry by service, topology and time; learns both within-domain signatures and between-domain dependencies; models propagation across shared network resources; and converts anomaly scores into service-impact hypotheses that engineers can verify. Particular attention is given to incomplete labels, changing baselines, class imbalance