Enhancing Security in Software-Defined Networks Using Artificial Intelligence Techniques - Rafiullah Haqmal; Mohammad Wasim Safi; Fida Mohammad (Department of Informatics, Universitas Malikussaleh, 2026)
Software-defined networking (SDN) has transformed network architectures through centralised control and programmability; yet this very centralization quite convincingly a double-edged sword exposes a set of critical vulnerabilities, including controller-targeted distributed denial-of-service attacks, exploits at the southbound interface, and flow rule manipulations that traditional defences continue to struggle to counter effectively. What is more, the rapid proliferation of IoT, 5G, and cloud integrations has only amplified these risks rendering static security mechanisms markedly inadequate in the face of continually evolving threats. This systematic review aims to investigate the efficacy of artificial intelligence techniques particularly machine learning and deep reinforcement learning in enhancing SDN security. Indeed, by adopting a rigorous systematic literature review methodology, the study will synthesise peer-reviewed works from major academic databases published between 2014 and 2025, critically evaluating AI-driven approaches for threat detection, anomaly identification, and automated mitigation while simultaneously identifying integration challenges such as scalability, real-time performance, and adversarial robustness. What is more, expected outcomes include the development of a comprehensive taxonomy of AI applications in SDN security, comparative insights into their reported performance (quite useful for highlighting strengths and weaknesses), and the identification of evidence-based research gaps. For that matter, the review will conclude by proposing future research directions oriented towards resilient and adaptive frameworks capable of safeguarding next-generation software-defined networks.