Detecting and diagnosing anomalies in cellular networks using Random Neural Networks

Despite a large body of literature and methods devoted to the analysis of network traffic, the automatic detection and classification of network traffic anomalies still represents a major issue for network operators. The problem becomes even more challenging for cellular ISPs, both due to the ever growing number of connected devices and to the constant deployment of new applications and services prone to performance issues. In this paper we tackle this problem using Machine Learning (ML) approaches: in particular, we devise a system based on Neural Networks to unveil the relations between several monitored traffic features and network anomalies impacting a large number of customers in an operational cellular network. By training a model based on Random Neural Networks (RNN), we provide a fast and accurate anomaly detector and classifier, capable to pinpoint anomalies without assuming any specific traffic model or particular network behavior. The proposed solution is evaluated using synthetically generated data from an operational cellular ISP, drawn from real traffic statistics to resemble the real cellular network traffic. Our RNN model is capable to detect and classify different classes of anomalies with high accuracy and low false alarm rates, even when the volume of such anomalies is small.