Learning at the Edge: Simulated DDoS Detection in 5G Networks

The growing use of 5G networks for critical services makes them vulnerable to Distributed Denial of Service (DDoS) attacks. While numerous Machine Learning (ML)-based approaches have been proposed, the real-world deployability of these models remains understudied. This work presents what is, based on existing literature, the first simulation-driven methodology to evaluate both the transferability and the operational feasibility of ML-driven DDoS detection in realistic 5G Multi-Access Edge Computing (MEC) settings. The study assess the cross-scenario performance of two state-of-the-art Convolutional Neural Network (CNN) DDoS detection models using three diverse datasets, including synthetic traffic representative of 5G environments. Leveraging the full 5G network simulator Simu5G, the study integrate the better-performing model into an MEC application to demonstrate a functional end-to-end pipeline from offline training to live attack mitigation. This approach delivers a reproducible framework for testing ML-based network defenses under realistic yet controllable conditions, enabling systematic evaluation beyond static benchmarks. The results confirm the feasibility of assessing the practical resilience of ML-driven DDoS defenses in 5G networks, with several areas identified for further optimization, including expansion of attack scenarios, enhancement of model robustness across datasets, and refinement of deployment strategies within the simulation environment.