Context-Aware Graph Attention for Unsupervised Telco Anomaly Detection

TL;DR

This paper introduces C-MTAD-GAT, an unsupervised, context-aware graph attention model for anomaly detection in mobile network time series, outperforming existing methods and proven effective in real-world deployment.

Contribution

The paper presents a novel unsupervised graph attention model that incorporates context embeddings and calibration techniques for improved anomaly detection in telecom networks.

Findings

C-MTAD-GAT outperforms MTAD-GAT and DC-VAE on TELCO dataset.

It achieves higher event-level and pointwise F1 scores.

The model triggers fewer false alarms in real deployment.

Abstract

We propose C-MTAD-GAT, an \emph{unsupervised}, \emph{context-aware} graph-attention model for anomaly detection in multivariate time series from mobile networks. C-MTAD-GAT combines graph attention with lightweight context embeddings, and uses a deterministic reconstruction head and multi-step forecaster to produce anomaly scores. Detection thresholds are calibrated \emph{without labels} from validation residuals, keeping the pipeline fully unsupervised. On the public TELCO dataset, C-MTAD-GAT consistently outperforms MTAD-GAT and the Telco-specific DC-VAE, two state-of-the-art baselines, in both event-level and pointwise F1, while triggering substantially fewer alarms. C-MTAD-GAT is also deployed in the Core network of a national mobile operator, demonstrating its resilience in real industrial settings.