Optimized Random Forest for DDoS Attack Detection in SDN Environment

Abstract

Software Defined Network (SDN) is a new type of network architecture that realizes network virtualization, with the characteristics of the control and forwarding separation, open programming, centralized control, and its flexibility is more suitable for the current complex and changeable network environment. However, due to its centralized control characteristics, the controller is faced with a huge risk of being subjected to distributed denial of service (DDoS) attacks that will cause the entire network to be paralyzed. Therefore, the detection of DDoS attacks in SDN networks has become the research direction of many scholars. so an algorithm for detecting DDoS attacks in SDN networks using optimizing RFs is proposed. By selecting the appropriate traffic features, creating the traffic dataset in the SDN environment, and using the dataset to optimize the model parameters, the attack detection model is constructed, and the final detection algorithm is as accurate as 99.98% for the collected dataset, which is more accurate and efficient than the common machine learning algorithms such as SVC and KNN.