Application identification system for SDN QoS based on machine learning and DNS responses

In recent years, the demand for application-specific qualify of service (QoS) management has grown. To effectively do application-specific QoS, a system albe to do flow classification at the application level is required. This paper presents an application identification system that can be integrated with a QoS management system in a software defined network (SDN). This paper describes the method to obtain ground truth (label) of the flow from four mainstream operating systems (OS), and the method to classify flow based on supervised machine learning and DNS responses. In our experiment, average F-measure of all applications reached 93.48%. The testing data set contained 294 applications, given that each platform version or execution file of an application was one application. The testing data set included Skype, Facebook, and other popular applications. Results showed that this system can identify application traffic on different platforms with high accuracy.