Advancing Predictive Security for Consumer Applications in Beyond 5G/6G Networks With Annotated Datasets

The evolution of Beyond 5G (B5G) and 6G networks introduces new opportunities for consumer-centric applications, requiring robust predictive security measures to maintain reliability. A critical component in the B5G landscape is the Network Data Analytics Function (NWDAF), a Network Function (NF) of the 5G Core introduced by the 3rd Generation Partnership Project (3GPP) in Rel. 15, designed to provide data analytics capabilities to the cellular network. This study focuses on detecting Distributed Denial of Service (DDoS) attacks, leveraging a comprehensive dataset collected from a 5G testbed. We evaluate deep learning models-Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Multi-Layer Perceptron (MLP)-and compare their performance with eXtreme Gradient Boosting (XGBoost), a machine learning technique based on gradient boosting, and Z-Score, a statistical method that quantifies how far a data point deviates from the mean. Results demonstrate that XGBoost achieves the highest F1-score of 0.97, precision of 0.96, and recall of 0.98, making it the preferred solution for identifying DDoS attacks from 5G network features, while also offering a computationally efficient solution for real-time applications. To improve interpretability, SHapley Additive exPlanations (SHAP) analysis identifies the network features influencing model decisions. The publicly available dataset used in our study supports further research in anomaly detection and provides valuable insights for future 6G applications, including immersive consumer experiences and autonomous services, while addressing emerging cyber threats.