Finally, we evaluate the SDN defense system. The mitigation model uses IP traceback to locate the attacker and effectively filters out abnormal traffic by sending flow rule commands from the controller. The anomaly detection model uses a lightweight hybrid deep learning method-Convolutional Neural Network and Extreme Learning Machine (CNN-ELM) for anomaly detection of traffic. The SDN defense system consists of two modules: anomaly detection module and mitigation module. To solve this problem, this paper designs and implements an online attack detection and mitigation SDN defense system. Since SDN has the characteristics of centralized management and programmable, attackers can easily take advantage of the security vulnerabilities of SDN to carry out distributed denial of service (DDoS) attacks, which will cause the memory of controllers and switches to be occupied, network bandwidth and server resources to be exhausted, affecting the use of normal users. With the development of Software Defined Networking (SDN), its security is becoming increasingly important.
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