Distributed abnormal traffic detection method for SDN based on deep learning - WANG Kun; FU Yu; DUAN Xueyuan; YU Yihan; LIU Taotao (Tongxin xuebao)
Addressing the high computational expenses, congested shared links, and propensity for single-point failures in network devices that can lead to a degradation of software defined network (SDN) service quality or even network paralysis during the execution of large-scale SDN detection tasks by traditional abnormal traffic detection methods, a distributed abnormal traffic detection method for SDN based on deep learning was proposed. This method constructed a “one-to-many” distributed generative adversarial network (D-VAE-WGAN) with a discriminator deployed on a cloud server and multiple generators deployed on SDN controllers. Utilizing normal traffic samples, collaborative training of the D-VAE-WGAN was completed, resulting in independent abnormal traffic detection proxies on controllers, enabling distributed detection of abnormal traffic within each controller's subnet in a large-scale SDN environment. Experimental results indicate that this method can rapidly and accurately detect abnormal samples in large-scale SDN, outperforming traditional methods in detection metrics such as accuracy and recall rate, and can detect unknown anomalies.