The evolution of information technologies has led to an exponential expansion of data and a proliferation of connected devices. In response to this evolution, Software-Defined Networks (SDN) have been deployed to overcome the limitations of traditional architectures in processing and managing data flowing through networks. The separation of the control plane and the data plane offers a more flexible and dynamic approach to monitoring network traffic. The integration of Edge Computing into IoT systems enhances real-time data processing, while effectively addressing security and privacy challenges. However, the resource limitations of Edge devices present significant challenges in protecting data against various types of attacks. Several studies indicate that the adoption of Machine Learning (ML) and its algorithms in SDN based Edge architectures helps overcome these resource constraints while strengthening system security.