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• W4386336947 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." @default.
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• W4386336947 date "2023-01-01" @default.
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• W4386336947 title "Machine Learning Applied to Anomaly Detection on 5G O-RAN Architecture" @default.
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• W4386336947 doi "https://doi.org/10.1016/j.procs.2023.08.146" @default.
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