@techreport{oai:ipsj.ixsq.nii.ac.jp:00186272,
 author = {Wassapon, Watanakeesuntorn and Kohei, Ichikawa and Hajimu, Iida and Wassapon, Watanakeesuntorn and Kohei, Ichikawa and Hajimu, Iida},
 issue = {34},
 month = {Feb},
 note = {The controller of the SDN network is a single point of failure. When the controller is down, the SDN network may stop working. The controller can be attacked by an attacker with DDoS attack techniques. In this paper, we propose a DDoS detection in the OpenFlow network by analyzing the OpenFlow messages in control plane by using machine learning. For the purpose, we use a DRAPA's DDoS traffic dataset for training and evaluating the proposed model. The DRAPA dataset only contains the traffic data of data plane, however we generate a dataset in control plane by simulating the network traffic from the dataset, and use the generated dataset for training and evaluation. In addition, we plan to integrate the proposed mechanism with our ”Opimon”, OpenFlow Interactive Monitoring Tool, to monitor and detect DDoS attack in real-time., The controller of the SDN network is a single point of failure. When the controller is down, the SDN network may stop working. The controller can be attacked by an attacker with DDoS attack techniques. In this paper, we propose a DDoS detection in the OpenFlow network by analyzing the OpenFlow messages in control plane by using machine learning. For the purpose, we use a DRAPA's DDoS traffic dataset for training and evaluating the proposed model. The DRAPA dataset only contains the traffic data of data plane, however we generate a dataset in control plane by simulating the network traffic from the dataset, and use the generated dataset for training and evaluation. In addition, we plan to integrate the proposed mechanism with our ”Opimon”, OpenFlow Interactive Monitoring Tool, to monitor and detect DDoS attack in real-time.},
 title = {A proposal of a real-time OpenFlow DDoS detection tool},
 year = {2018}
}