Abnormal Behavior Detection Based on Traffic Pattern Categorization in Mobile Networks

Abnormal behavior in mobile cellular networks can cause network faults and consequent cell outages, a major reason for operational cost increase and revenue loss for operators. Nonetheless, network faults and cell outages can be avoided by monitoring abnormal situations in the network and acting accordingly. Thus, anomaly detection is an important component of self-healing control and network management. Network operators may use the detected abnormal behavior to quantify numerically their intensity. The quantification of abnormal behavior assists the characterization of potential regions for infrastructure updates and to support the creation of public policies for local connectivity enhancements. We propose an unsupervised learning solution for anomaly detection in mobile networks using Call Detail Records (CDR) data. We evaluate our solution using a real CDR data set provided by an Italian operator and compare it against other state-of-the-art solutions, showing a performance improvement of around 35%. We also demonstrate the relevance of considering the distinct traffic patterns of diverging geographic areas for anomaly detection in mobile networks, an aspect often ignored in the literature.

Automated summary

Abnormal behavior in mobile cellular networks can lead to network faults and cell outages, causing operational cost increase and revenue loss for operators. Monitoring and quantifying abnormal behavior is important for self-healing control, infrastructure updates, and public policy creation. Using a unsupervised learning solution for anomaly detection in mobile networks can improve performance and consider diverse geographic traffic patterns often overlooked in existing literature.