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This paper proposes a Federated Learning-based DDoS detection mechanism for Software Defined Networks (SDNs) to address the limitations of traditional centralized intrusion detection systems. By allowing multiple clients to independently train local models and share only model updates, the approach enhances privacy, scalability, and detection accuracy, achieving 99.5% accuracy and 100% precision. The proposed framework utilizes a ranking-based aggregation method to improve robustness against non-IID data di

Author

b22ai030

Language

EN