Research on DDoS Abnormal Traffic Detection Under SDN Network 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.

A Framework for Distributed Denial of Service Attack Detection and Reactive Countermeasure in Software Defined Network Software Defined Network is an evolving and promising architecture which allows greater control over network entities by centralising the control plane. Although on the surface SDN provides a simple framework for network programmability and monitoring, few has been said about security measures to make it more robust to hitherto security flaws. Among the identified security flaws, DDoS flooding attack continue to be one of the major security concerns as attack volumes are increasing year on year. In this paper, we developed and implement the feasibility of spoofing and flooding DDoS attack on data plane devices using Mininet emulator, floodlight and network performance testing tools. We further developed a mitigation mechanism to counter these attacks by pushing reactive flow through the controller to the attacking switch port. Our result shows that pushing flows through the controller mitigates the flooding attack with low performance overheads, and requires no change to the controllers mode of operation for deployment, which indicates a good performance of our model.