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

Atanas Vlahov, Vladimir Poulkov, Pavlos Lazaridis, Zaharias Zaharis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publication28th European Wireless Conference, EW 2023
PublisherVDE Verlag GmbH
Number of pages5
ISBN (Electronic)9783800762262, 9783800762255
Publication statusPublished - 2 Oct 2023
Event28th European Wireless Conference - Rome, Italy
Duration: 2 Oct 20234 Oct 2023
Conference number: 28


Conference28th European Wireless Conference
Abbreviated titleEW 2023

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