Big Data Driven Anomaly Detection for Cellular Networks

Abstract

Anomaly detection for large scale cellular networks can be used by network operators to optimize network performance and enhance mobile user experience. This paper aims at detecting user anomalies from spatio-temporal cell phone activity data. We design an approach combining time series analysis and machine learning to extract the traffic patterns of areal units. This approach can cluster areal units with similar traffic patterns and segment a city into distinct groups. Then, in grouped-areas, we use a clustering technique to detect anomalous behaviors of the cellular network and verify the accuracy of the results using ground truth information collected from online sources. The results indicate that anomalies are associated with abruptly high or unexpected traffic demand at a specific location and time. In addition, we obtain anomaly-free data by removing anomalous data and train a decomposed traffic prediction model. It is observed that the prediction model trained with anomaly-free data can achieve lower normalized mean square error (NMSE), i.e., higher prediction accuracy, than the model trained with anomalous data.