A Machine Learning Methodology for Network Anomalies Detection in O-RAN Networks

Despite the extensive research and various existing systems for detecting and classifying anomalies in operational networks, Internet Service Providers (ISPs) continue to seek efficient methods to handle the increasing number of network traffic anomalies they encounter in their daily operations. This research paper tackles the challenge of automatically detecting network traffic anomalies using Machine Learning (ML) techniques. We introduce a straightforward classification approach based on Deep Neural Networks and assess its accuracy, precision, and recall performance. To evaluate the proposed neural network, we train and test it using data collected from a real LTE deployment of 10 base stations.