Supervised and deep learning techniques for DDoS detection in software-defined network architectures: a systematic review

Software-Defined Networking (SDN) offers significant advantages over traditional network architectures by providing flexibility, programmability and centralized control in network management. However, the centralized nature of this architecture brings new vulnerabilities, especially against security threats such as Distributed Denial of Service (DDoS) attacks. In this context, Machine Learning (ML) based methods offer effective and innovative solutions for detecting DDoS attacks in SDN environments.This paper presents a comprehensive review of machine learning techniques for DDoS attack detection in SDN-based networks. The most remarkable aspect is that, unlike many existing works in the literature, it does not only focus on general detection methods, but also examines in detail various scenarios in different application areas of SDN, such as Internet of Things (IoT), SCADA systems, 5G and mobile networks, and vehicular ad-hoc networks (VANET). This provides a holistic perspective on the security dynamics of SDN architecture in different contexts and comparatively evaluates current threats and solution approaches in these areas.In the study, the success, usage areas and limitations of different machine learning algorithms (supervised, unsupervised and deep learning methods) in detecting DDoS attacks are analyzed and conclusions are made to guide researchers. In this respect, the study contributes to the literature on SDN security in terms of both technical depth and application diversity.