An Exhaustive Parametric Analysis for Securing SDN Through Traditional, AI/ML, and Blockchain Approaches: A Systematic Review

• Published in: The International journal of networked and distributed computing (Online) Vol. 13; no. 1
• Main Authors: Shahzad, Muhammad, Rizvi, Safdar, Khan, Talha Ahmed, Ahmad, Sadique, Ateya, Abdelhamied A.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.06.2025
• Subjects: Computer Science; Review Article; Software defined networks; SDN security; Artificial intelligence; Blockchain
• ISSN: 2211-7938; 2211-7946
• DOI: 10.1007/s44227-024-00055-8

Technology has revolutionized various aspects of human life, and networking plays an important role in this revolution. Networks have gained significant importance with the evolution of technology. Traditional networks have served human life for decades but cannot provide future networking requirements. Software-defined networking (SDN) is a major paradigm shift in the networking field; it provides an efficient way of managing and controlling networks by separating the control layer from the data layer. SDN has gained significant attention from researchers and industry experts in recent years. However, to accept SDN as a replacement for traditional networking, several areas need to be improved, and security is one of them. This exhaustive literature survey presents a detailed analysis of security solutions proposed by researchers for SDN security. This literature review can be classified into three main categories of SDN security solutions: conventional network security solutions, machine learning (ML)-based, and blockchain-based security solutions. A detailed domain analysis has been performed, and the proposed strategies and their results have been evaluated. Moreover, the shortcomings and contributions of previous approaches toward scientific society have been analyzed to facilitate the new research. This research has completed a detailed and diversified literature review to have wide concrete knowledge regarding the future trends for designing and developing innovative SDN security solutions. An exhaustive literature review revealed that various ML-based SDN security approaches have been proposed, including convolutional neural network (CNN), support vector machine (SVM), and k-nearest neighbors (KNN). Among these ML algorithms, CNN and SVM were found to be more effective and state-of-the-art in securing the SDN. Parametric analysis of different researchers demonstrated that blockchain-based flow rules, blockchain-based packet parsers, and controller authentication outperformed while securing the SDN networks. The paper covered all the aspects of previous research that have been experimented with to determine the robust and concrete techniques for the security of SDN networks.