Abstract
A machine learning based Distributed Denial of Service (DDoS) attack detection system, implemented in a virtual SDN environment testbed, has been presented in this paper. This system identifies whether any incoming traffic in a network is a DDoS type or not. To implement this approach, we applied AdaBoosting with decision stump as a weak classifier to train our model on a private network dataset in SDN environment. Our model showed up to 93% detection accuracy with a low false-positive rate. We have also tested and compared our model’s accuracy with different machine learning algorithms and presented the result.
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Sen, S., Gupta, K.D., Manjurul Ahsan, M. (2020). Leveraging Machine Learning Approach to Setup Software-Defined Network(SDN) Controller Rules During DDoS Attack. In: Uddin, M., Bansal, J. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-7564-4_5
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DOI: https://doi.org/10.1007/978-981-13-7564-4_5
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