SDN-Enabled IoT Security Frameworks—A Review of Existing Challenges

This comprehensive systematic review examines the integration of software-defined networking (SDN) with IoT security frameworks, analyzing recent advancements in encryption, authentication, access control techniques, and intrusion detection systems. Our analysis reveals that while SDN demonstrates promising capabilities in enhancing IoT security through centralized control and dynamic policy enforcement, several critical limitations persist, particularly in scalability and real-world validation. As intrusion detection represents an integral security requirement for robust IoT frameworks, we conduct an in-depth evaluation of Machine Learning (ML) and Deep Learning (DL) techniques that have emerged as predominant approaches for threat detection in SDN-enabled IoT environments. The review categorizes and analyzes these ML/DL implementations across various architectural paradigms, identifying patterns in their effectiveness for different security contexts. Furthermore, recognizing that the performance of these ML/DL models critically depends on training data quality, we evaluate existing IoT security datasets, identifying significant gaps in representing contemporary attack vectors and realistic IoT environments. A key finding indicates that hybrid architectures integrating cloud–edge–fog computing demonstrate superior performance in distributing security workloads compared to single-tier implementations. Based on this systematic analysis, we propose key future research directions, including adaptive zero-trust architectures, federated machine learning for distributed security, and comprehensive dataset creation methodologies, that address current limitations in IoT security research.