Biblio

This paper studies Distributed Denial of Service (DDoS) attack detection by adopting the Deep Neural Network (DNN) model in Software Defined Networking (SDN). We first deploy the flow collector module to collect the flow table entries. Considering the detection efficiency of the DNN model, we also design some features manually in addition to the features automatically obtained by the flow table. Then we use the preprocessed data to train the DNN model and make a prediction. The overall detection framework is deployed in the SDN controller. The experiment results illustrate DNN model has higher accuracy in identifying attack traffic than machine learning algorithms, which lays a foundation for the defense against DDoS attack.