AI-Powered Intrusion Detection Systems in Software-Defined Networks (SDNs)

Abstract

The growing complexity and dynamism of modern networks have made traditional security mechanisms increasingly ineffective, particularly in the face of advanced and evolving cyber threats. Software-Defined Networking (SDN) offers centralized control and programmability, enabling more flexible network management but also introducing new attack surfaces. This paper explores the integration of Artificial Intelligence with Intrusion Detection Systems (IDS) in SDN environments to enhance real-time threat detection, classification, and response. Leveraging machine learning and deep learning algorithms such as Random Forests, Support Vector Machines (SVM), and Convolutional Neural Networks (CNN), the proposed AI-powered IDS architecture aims to detect both known and unknown intrusions, including those embedded in encrypted or obfuscated traffic. A comprehensive evaluation using benchmark datasets demonstrates improved accuracy, reduced false positive rates, and efficient scalability. The study also discusses the challenges of implementing AI in SDNs, such as data imbalance, model interpretability, and computational overhead, while proposing solutions to mitigate these limitations. By fusing the strengths of AI and SDN, this research contributes to the development of intelligent, adaptive, and robust cybersecurity frameworks for next-generation networks. 

Keywords: Software-Defined Networking (SDN), IDS, Random Forest, SVM, CNN