Distributed abnormal traffic detection method for SDN based on deep learning
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 pa-ralysis during the execution of large-scale SDN detection tasks by traditional abnormal traffic detection methods,a dis-tributed 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.
deep learningsoftware defined networkdistributedabnormal traffic detection
万方数据
维普
