DDoS attack detection in software defined networking controller using machine learning techniques

Abbas Jasem Altamemi, Aladdin Abdulhassan, Nawfal Turki Obeis


The term software defined networking (SDN) is a network model that contributes to redefining the network characteristics by making the components of this network programmable, monitoring the network faster and larger, operating with the networks from a central location, as well as the possibility of detecting fraudulent traffic and detecting special malfunctions in a simple and effective way. In addition, it is the land of many security threats that lead to the complete suspension of this network. To mitigate this attack this paper based on the use of machine learning techniques contribute to the rapid detection of these attacks and methods were evaluated detecting DDoS attacks and choosing the optimum accuracy for classifying these types within the SDN, the results showed that the proposed system provides the better results of accuracy to detect the DDos attack in SDN network as 99.90% accuracy of Decision Tree (DT) algorithm.


DDoS attacks; Feature selection; Logistic regression; Machine learning; Software defined networking

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DOI: https://doi.org/10.11591/eei.v11i5.4155


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