Deep Convolutional Neural Network-Based High-Precision and Speed DDOS Detection in SDN Environments

The rapid expansion of interconnected infrastructures has dramatically increased the volume of network devices and traffic, introducing significant security vulnerabilities in Software-Defined Networking (SDN) environments. Distributed Denial-of-Service (DDoS) attacks exploit the centralized control plane characteristic of SDN, underscoring the need for adaptive, efficient detection mechanisms. In response, we present a novel hybrid framework that leverages Principal Component Analysis (PCA) for dimensionality reduction and a supervised Deep Convolutional Neural Network (DCNN) for high-precision classification of normal and malicious events. Rigorous evaluation on a real-world SDN dataset, comprising both categorical and numerical features, demonstrates that the proposed model achieves an exceptional detection accuracy of 99.97% and a swift detection time of only 3 seconds-making it well-suited for real-time applications in SDN. These results affirm the efficacy of our model in enhancing both security and scalability, contributing a robust solution for mitigating DDoS threats in dynamic SDN environments.