Mitigation of DDoS Attacks in the Data Plane of Software-Defined Networking Using ML Techniques

Abstract

Distributed Denial-of-Service (DDoS) attacks remain one of the most significant cyber threats faced by Software-Defined Networking (SDN) architectures, essentially because of the salient decoupling of the control and data planes. This study examines the implications of DDoS attacks on the SDN data plane and evaluates the effectiveness of Machine Learning (ML) algorithms in detecting and addressing these attacks in real time. Using the Ryu controller, Mininet network emulator, and OpenFlow protocol, a realistic experimental environment was created to provide an accurate replica of the dynamic SDN behaviour under adverse circumstances. Empirical studies have demonstrated that distributed DDoS attacks, such as SYN, UDP, and ICMP flooding, substantially degrade network performance by reducing throughput, increasing packet loss, and exhausting switch flow table resources. To mitigate these effects, a suite of supervised machine learning classifiers, including Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes (NB), was instantiated and evaluated using traffic features captured on the emulated platform. The key performance indicators used to evaluate the classifiers included accuracy, precision, recall, and F1-score. The findings indicate that the Decision Tree and KNN models achieved detection rates above the 99% mark, with strong precision and recall scores, which in turn highlights their suitability for implementation in SDN-based security systems. This study provides experimental evidence that ML-based intrusion detection mechanisms can significantly enhance the resilience of SDNs to volumetric attacks. These results promote the implementation of adaptive and responsive security modules in SDN controllers, thereby increasing network resilience, particularly in large and dynamically programmable networks.