Machine Learning-Based Real-Time Detection and Mitigation of DoS Attacks in SDN-Based 5G Network

Multi-Access Edge Computing (MEC) is a fundamental component for 5G networks to overcome the latency limitations of traditional cloud computing. However, bringing resources closer to users exposes edge nodes to significant security threats, particularly volumetric Denial of Service (DoS) attacks. Current defenses often depend on static thresholds or computationally expensive deep learning, which can exhaust the limited resources of MEC nodes. To address these limitations, this paper proposes a resource-optimized edge-centric security management logic that integrates Software Defined Network (SDN) with lightweight supervised learning (C5.0, Bagging-CART, and Random Forest). Unlike standard system integrations, we introduce a dynamic non-permanent blocking algorithm designed to balance detection accuracy with control plane stability. Experimental results demonstrate that the proposed C5.0 model, operating at a specific 0.20% sFlow sampling point, achieves 100% detection accuracy with under 100 ms mitigation latency. The system successfully reduces volumetric attack loads from 445 Mbps to 95 Mbps (a 78% reduction) at the node level. These findings confirm that the proposed framework achieves higher computational efficiency than complex alternatives, making it a highly stable solution for constrained 5G MEC environments.