Abstract
Software Defined Networking (SDN) centrally manages the network data layer to improve the programmability and flexibility of networks by the controller. Because of centralized control, SDN is vulnerable to Distributed Denial of Service (DDoS) attacks. In order to protect the security of SDN, a method based on K-means++ and Fast K-Nearest Neighbors (K-FKNN) is proposed for DDoS detection in SDN, and the modular detection system is presented in the controller. The detailed experiments are conducted to evaluate the system performance. The results of the experiments show that K-FKNN improves the detection accuracy and efficiency of K-Nearest Neighbors (KNN), and has high precision and stability of DDoS detection in SDN.
Highlights
The technology development of cloud computing and big data accelerates network expansion
In Software Defined Networking (SDN), a large amount of Distributed Denial of Service (DDoS) attacks are sent to the victims, the switch can’t find a match for the most part, and the information parcel is sent to the controller
Our contributions are summarized as follows: 1) A DDoS detection method based on K-means++ and Fast K-Nearest Neighbors (K-FKNN) is proposed to improve the detection efficiency and accuracy of KNN in SDN, and the time complexity of the proposed algorithm is analyzed
Summary
The technology development of cloud computing and big data accelerates network expansion. In SDN, a large amount of DDoS attacks are sent to the victims, the switch can’t find a match for the most part, and the information parcel is sent to the controller. Various types of machine learning methods are applied in DDoS detection of SDN, this will be detailed . Our contributions are summarized as follows: 1) A DDoS detection method based on K-means++ and Fast K-Nearest Neighbors (K-FKNN) is proposed to improve the detection efficiency and accuracy of KNN in SDN, and the time complexity of the proposed algorithm is analyzed. The remainder of this paper is structured as follows: In the section II, the related work of machine learning methods applied in DDoS detection of SDN is introduced.
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