Hafiz Bilal Ahmad¹ Haichang Gao¹ and Fawwad Hassan Jaskani², ¹Xidian University, China, ²Islamia University, Pakistan

In order to meet the specific requirements of various industries and the stringent demands of 5G, the control and management of 5G networks will heavily depend on the integration of Software Defined Networking, Network Function Virtualization, and Machine Learning. Machine learning can play a crucial role in addressing challenges such as slice type prediction, route optimization, and resource management. To effectively evaluate the use of machine learning in 5G networks, a suitable testing environment is necessary. This study proposes a lightweight testbed that leverages container virtualization technologies to support the development of machine learning net-work functions within 5G networks. The Deep Slice 5G dataset from Kaggle was utilized to predict the type of communication between users based on packet loss and delay budget ratio, with the goal of making 5G systems more efficient. To accomplish this, we applied several Boosted Machine Learning models such as XGBoost, Gradient Boost, AdaBoost, LightGradientBoosting, CatBoost, and HistGradientBoosting. After evaluation, the Catboost model demonstrated the highest accuracy of 99% in identifying the correct slice of 5G based on the selected features of the dataset.

5G Network, Machine Learning, Intrusion Detection System.