Network Anomaly Detection for a Leading Telecom Provider
The Challenge
A large telecom operator was experiencing network degradation events that were only detected after customer complaints, resulting in SLA breaches and reactive maintenance costs. The existing monitoring system generated thousands of alerts daily with no intelligent prioritisation.
Environment
Multi-vendor network infrastructure generating over 50 million telemetry events per day across core, transport and access network layers. Data was siloed across multiple OSS/BSS systems with no unified view.
Our Solution
We designed and deployed a real-time anomaly detection platform that ingests network telemetry via Apache Kafka, applies ML-based anomaly scoring and surfaces prioritised alerts to network operations teams through an operational dashboard.
Approach
Unsupervised anomaly detection using Isolation Forest and LSTM-based time-series models trained on 18 months of historical telemetry. Models were deployed as containerised inference services with automated retraining pipelines.
Architecture
Kafka-based streaming ingestion, Spark Structured Streaming for feature computation, ML inference layer on Kubernetes, PostgreSQL for alert storage, React-based NOC dashboard.
Outcomes
- Reduced mean time to detect (MTTD) network anomalies by approximately 70%
- Decreased false-positive alert volume by over 60% through ML-based filtering
- Enabled proactive maintenance scheduling before customer-impacting events