DDoS Detection in SDN using Deep Learning

In this paper, we present a deep learning approach for the detection of Distributed Denial of Service (DDoS) attacks within Software-Defined Networking (SDN) environments. The escalating threat of DDoS attacks poses significant challenges to SDN security, necessitating innovative detection methods. Our approach leverages a Multi-Layer Perceptron (MLP) model trained on a comprehensive SDN traffic dataset, exhibiting enhanced accuracy and efficiency compared to traditional machine learning algorithms. Integration with the Ryu controller facilitates real-time DDoS attack detection in live SDN environments, showcasing the practical applicability of deep learning in enhancing network security. We emphasize the creation of a robust SDN traffic dataset that enables rigorous evaluation and comparison of detection techniques, addressing a critical gap in current research. Through advancements in deep learning, our study underscores the importance of developing sophisticated security mechanisms to safeguard SDN architectures against evolving cyber threats. The effectiveness of our proposed method signifies a substantial contribution to the field, promoting the integrity and availability of network resources amidst increasing DDoS vulnerabilities. Our work not only highlights the technical prowess of deep learning in SDN security but also underscores the imperative for ongoing research to refine and optimize detection methods. By addressing key limitations and exploring hybrid approaches, we aim to fortify SDN networks against malicious activities, paving the way for robust and adaptive security solutions in dynamic network environments.