Software Defined Network (SDN) Controller for 5G Networks

The 5G network, with its high speed, large capacity, and low latency characteristics, provides a vast space for various innovative services and applications. However, at the same time, it also makes the means of network attacks more complex and varied. Therefore, network intrusion detection (NID) technology has become an urgent research field that needs to be strengthened. Software Defined Networking (SDN), as a new type of network architecture, can flexibly support diversified services and business models of 5G networks through its virtualization and network slicing technology. SDN not only enables dynamic configuration and optimization of network resources, but also provides differentiated technical solutions in terms of functionality, performance, and security protection, providing new solutions for network security. This article proposes an SDN control algorithm based on deep learning (DL), aiming to achieve real-time monitoring and anomaly detection of network traffic through the powerful data processing and analysis capabilities of DL, thereby improving the accuracy and efficiency of NID. The experimental results show that the algorithm has significant advantages in detecting network intrusions, not only improving the accuracy of detection, but also reducing the false alarm rate, providing strong technical support for the security protection of 5G networks.