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@article{170450,
        author = {Gunjani J. Vaghela and Simran A. Sodha and Punit C. Trivedi},
        title = {An analysis on Federated Learning Approaches for Scalable DDoS Protection in Multi-Controller SDNs},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {1098-1102},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170450},
        abstract = {Software-Defined Networking (SDN) with multi-controller architectures faces significant challenges in detecting Distributed Denial of Service (DDoS) attacks due to the decentralized nature of network control and the volume of data traffic. Traditional approaches struggle to scale efficiently while maintaining high security and privacy standards. This survey paper explores the use of Federated Learning (FL) as an innovative solution for adaptive DDoS attack detection in SDN environments. We review the integration of FL with machine learning (ML) and deep learning (DL) models, highlighting their potential to provide decentralized, privacy-preserving detection mechanisms. Additionally, we examine existing methodologies, evaluate their strengths and weaknesses, and discuss the future research directions, such as improving model accuracy, overcoming communication overhead, and addressing security challenges in large-scale SDN deployments. By combining FL with SDN, this approach promises to enhance DDoS detection systems' scalability and efficiency, offering a robust solution for modern network infrastructures.},
        keywords = {Adaptive DDoS Detection, Federated Learning (FL), Multi-Controller Architectures, Software-Defined Networking (SDN)},
        month = {December},
        }