Real-time SARIMA-based anomaly detection in operator networks

The growing complexity of telecommunication networks increases the need for automated monitoring and detection of incidents. The increasing amount of data generated by current information and communication technologies equipment can be used to identify abnormal behavior and detect anomalies. In this paper, we propose a volume-based real-time solution for anomaly detection in operator networks. The proposal is based on a seasonal autoregressive integrated moving average (SARIMA) forecasting of time-series data, and the identification of outliers. We validate the proposed solution using real traffic traces from a nationwide operator’s network which we make publicly available. Our experimental results show that the proposed solution outperforms baseline approaches in terms of precision, being able to substantially reduce the amount of false positives, while maintaining the same level of recall.