A Survey on Proactive Security in Software Defined Networks Using a Convolutional Neural Network-Based Early Warning System

Software-Defined Networking (SDN) centralizes control and improves programmability, but this also increases vulnerability to large-scale threats such as DDoS. Conventional intrusion detection systems often fail in this context due to static rules and high false alarms. Recent studies apply deep learning for anomaly detection, with Convolutional Neural Networks (CNNs) offering faster training and lower latency than recurrent or hybrid models. This survey reviews CNN-based IDS approaches and compares them with alternatives like DNNs [1], GRU-RNNs [2], and SAE-based models [3]. Our analysis highlights CNNs’ faster convergence and adaptability for real-time detection, while also identifying the challenges of scalability, data diversity, and deployment overhead. The paper concludes by outlining open research directions toward intelligent, lightweight, and automated IDS solutions for strengthening SDN environments.