(untitled) (Al-Mansour Journal, 2026)
The rapid development of network technologies and the growing sophistication of cyber threats have emphasized the necessity of smart, scalable and privacy-sensitive security solutions. Software-Defined Networking (SDN) is a system that offers centralized control and visibility of the network globally and as such is a fitting platform on which to build complex security mechanisms. Nevertheless, SDN-based traditional intrusion detection methods usually involve a centralized amount of data collection, posing serious questions about the privacy of data and scaling. This paper suggests designing a privacy-sensitive Artificial Intelligence (AI)-controlled SDN controller with Federated Learning (FL) to detect intrusions in a distributed manner. The SDN controller in the proposed architecture is complemented with smart modules, comprising of intrusion detection system (IDS), federated learning aggregation unit, and a decision engine. The distributed controllers are trained on their own data to obtain a local model (Deep Neural Network) and only the model parameters are communicated to a central controller to be aggregated. The model is tested with the help of the NSL-KDD dataset, in which the network traffic is categorized as normal and malicious. Experimental outcomes prove that the suggested FL-based model has high detection capabilities, with the accuracy of 96% and precision of over 90, which is better than the performance of the traditional non-federated models. The suggested system is successful in terms of data privacy, lowering the communication overhead, and increasing the scalability with a high detection accuracy. These findings suggest the implementation of federated learning in an AI-based SDN controller offers a powerful and effective framework to current network security.