Abstract
Software Defined Networking(SDN) focuses on overcoming the drawbacks of traditional networks and offers the advantage of flexibility in managing the networks. On the other hand, this new paradigm makes networks susceptible to attacks. DDoS is one of those significant attacks. DDoS makes resources unavailable to legitimate users, and one of the mechanisms that attackers follow is the TCP-SYN flood to launch the DDoS attack. The TCP SYN flood attack takes advantage of the three-way handshake to exhaust the web server’s resources. We proposed an approach to detect DDoS attacks in SDN based on an ensemble technique.Our proposed approach uses stacking model, combining bagging and boosting models as ensembled techniques. we implemented our proposed approach on dataset. We have generated our own dataset containing the required features. We show that our proposed approach gives better accuracy than existing models in the literature. We validated our proposed approach on both generated dataset and existing dataset.
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Tarakanadha Reddy, P., Shalini, P.V., Radha, V. (2023). Ensembled Machine Learning Techniques for DDoS Detection in SDN. In: Reddy, A.B., Nagini, S., Balas, V.E., Raju, K.S. (eds) Proceedings of Third International Conference on Advances in Computer Engineering and Communication Systems. Lecture Notes in Networks and Systems, vol 612. Springer, Singapore. https://doi.org/10.1007/978-981-19-9228-5_32
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DOI: https://doi.org/10.1007/978-981-19-9228-5_32
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