Detection and defense of DDoS attack–based on deep learning in OpenFlow‐based SDN

Summary Distributed denial of service (DDoS) is a special form of denial of service attack. In this paper, a DDoS detection model and defense system based on deep learning in Software‐Defined Network (SDN) environment are introduced. The model can learn patterns from sequences of network traffic and trace network attack activities in a historical manner. By using the defense system based on the model, the DDoS attack traffic can be effectively cleaned in Software‐Defined Network. The experimental results demonstrate the much better performance of our model compared with conventional machine learning ways. It also reduces the degree of dependence on environment, simplifies the real‐time update of detection system, and decreases the difficulty of upgrading or changing detection strategy. This paper introduces a DDoS detection model and defense system based on deep learning in Software Defined Network (SDN) environment. The model can learn patterns from sequences of network traffic and trace network attack activities in a historical manner. The experimental results demonstrate the much better performance of our model compared with conventional machine learning ways. Moreover, the implemented defense system based on the model can effectively clean the DDoS attack traffic.