Machine Learning Applied to Anomaly Detection on 5G O-RAN Architecture
Pedro V. A. Alves, Mateus A.S.S. Goldbarg, Wysterlânya K. P. Barros, Iago Rêgo, Vinicius Filho, Allan Martins, Vicente A. de Sousa Jr., Ramon dos Reis Fontes, Eduardo Aranha, Augusto Neto, Marcelo A. C. Fernandes
Abstract
This article presents a study with feasibility and performance analysis of machine learning (ML) techniques using supervised techniques for anomaly detection problems in a 5G communication network. The proposed ML models (Multilayer Perceptron, Decision Tree, and Support Vector Machine) were used to classify data into anomaly or non-anomaly based on two 5G Open Radio Access Network (O-RAN) datasets with various key performance indicators (KPIs). Furthermore, we propose a strategy that devotes to labeling anomalous situations, leveraging the t-Distributed Stochastic Neighbor Embedding (tSNE) technique atop datasets enclosing multiple KPIs. The results were significant, with an accuracy above 90% for all use cases considered.