Anomaly Detection in Mobile Networks

With the widespread usage of 4G technologies and the upcoming promise of 5G networks, there is a strong need for increased network performance and reliability. However, as these networks become bigger and faster, so does their complexity. Currently, network operators detect most of the network failures manually. This is a very time consuming and tedious task for them, oftentimes taking up to several hours. Thereby arises a need for an automated Anomaly Detection and Correction system. Such a system would be a step towards the ultimate goal of a cognitive self-organizing network. We here take the case of a mobile network with hundreds of key performance indicators, which generates huge amount of network logs every hour. Since user behavior has patterns in usage, e.g. weekdays network traffic will be higher than weekend's traffic near office areas, we analyze a Time Series (TS) Decomposition based approach, which takes into consideration of trends and seasonality in data. We also explore the use of a seasonal auto-regressive technique, SARIMA, for anomaly detection. Assuming that an anomalous behaviour is continuous in time, we evaluate a recurrent encoder-decoder based approach, MSCRED for Anomalous Window Detection. We do this analysis to find the KPI and the respective network element, whose behavior is abnormal. Our results show that while Time Series Decomposition outperforms SARIMA over single point anomaly detection, MSCRED significantly performs well in predicting anomalous time windows.