Research Question: This paper investigates into how machine learning can be applied for the purpose of detecting anomalies in the data describing transport component within the cellular network. Motivation: In the field of telecommunications, terabytes of data are generated each hour. This makes the manual analysis almost impossible to perform. There are thousands of components whose behaviour needs to be monitored, since anomalous behaviour could indicate a possible failure that can lead to network degradation, huge maintenance costs, and finally – a bad user experience. Our goal is to try to catch anomalous behaviour automatically, and thus help domain experts when performing drill down analysis of the degraded base stations and their key performance indicators (KPIs). Idea: The main idea of this paper is to empirically evaluate the application of machine learning for the problem of anomaly detection, in the field of telecommunications, specifically to long term evolution (LTE) networks. Data: Data used in the analysis contains information about base transceiver stations (BTS) behaviour through the time. The data are gathered from a cellular network provider located in Serbia. The data are collected on an hourly basis, for a period of two weeks, resulting in almost 700 thousand rows. The behaviour is assessed by 96 transport KPIs coming from BTS, describing the package losses, delays, transmission success rates, etc. Tools: Two main algorithms, ensemble-based Isolation Forest and autoencoder neural network, are elaborated and applied in order to identify patterns of anomalous behaviour. Findings: The results show that machine learning can be successfully applied in the field of LTE networks for the problem of anomaly detection. Machine learning can significantly reduce the time needed for the domain experts to identify anomalies within the network. In addition to time efficiency, one of the algorithms tested is able to identify anomalous KPIs separately, which is crucial when performing root cause analysis, by using drill-down approach, in order to identify which component is degraded. Contribution: This paper enriches existing research related to anomaly detection in LTE networks and provides an innovative approach to automated root-cause analysis of network degradation.
FLAGS: A methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning