Abstract
The seperation of control layer from data layer through SDN (software defined network) enables network administrators to plan the network programmatically without changing network devices, realizing flexible configuration of network devices and fast forwarding of data flows. However, due to its construction, SDN is vulnerable to be attacked by Distributed Denial of Service (DDoS) attack. So it is important to detect DDoS attack in SDN network. This paper presents a DDoS detection scheme based on the machine learning method of SVM (support vector machine) support vector machine in SDN environment. By extracting the flow table information features in SDN network, the data is detected and the data model of DDoS traffic can be trained, and the purpose of DDoS abnormal traffic identification is finally realized.
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Ma, Z., Huang, J. (2020). Research on DDoS Abnormal Traffic Detection Under SDN Network. In: Shen, H., Sang, Y. (eds) Parallel Architectures, Algorithms and Programming. PAAP 2019. Communications in Computer and Information Science, vol 1163. Springer, Singapore. https://doi.org/10.1007/978-981-15-2767-8_33
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DOI: https://doi.org/10.1007/978-981-15-2767-8_33
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