A Cloud-Integrated Federated Learning Approach for Secure and Private Intrusion Detection in SDN Environment

Software Defined Networking (SDN) has significantly improved network flexibility by separating the control plane from the data plane and enabling centralized network management. However, this centralized architecture also introduces serious security challenges, making SDN controllers attractive targets for cyberattacks. Traditional intrusion detection systems (IDS) are often ineffective in SDN environments due to their reliance on centralized data collection and static detection mechanisms, which raise privacy and scalability concerns in cloud-based and multi- tenant networks.