Informer-Based Anomaly Detection in Mobile Networks Using CDR Time-Series Analysis

This paper introduces an Informer model for anomaly detection in mobile networks. Call detail records, containing mobile internet traffic data, are thoroughly processed, and feature engineering is applied to improve the model’s overall accuracy and efficiency. Moreover, a logarithmic transformation is employed on the data to ensure realistic and precise results. After training the model for 5 epochs and testing with synthetic injected anomalous data patterns, an accuracy of 80.69% was achieved.