Deep Learning Autoencoder based Anomaly Detection Model on 4G Network Performance Data

Abstract

A 4G network stands for a fourth-generation mobile network that enables 4G capable mobile phones to connect with the internet faster than ever. It is possible because of faster authentication between mobile phone and network entity. The network entities are sophisticated and require constant monitoring in terms of fault management and performance management. However, the fault is very rare in that network nodes, but a deviation of performance is normal. This deviation is known as an anomaly, and machine learning is useful for detecting an anomaly. In this paper, deep neural network autoencoder-based anomaly detection is discussed over 4G network performance data. An autoencoder can mimic an output from its input and provide superior performance when the data properties are similar. Further elaboration in this paper is how different properties of autoencoder hidden layer count, variable threshold measurement etc influence the anomaly detection outcome of 4G network performance data. At last, an autoencoder configuration is recommended for anomaly detection of 4G network performance data.