Today, as the network environment increases, various types of traffic patterns generated for each application and service are generated, and traffic analysis methods that can classify traffic applications and services are being studied. In particular, Skype is a VoIP service that is serviced by Microsoft and is currently the most widely used internationally. For this reason, the importance of Skype traffic detection is growing in terms of network management. In order to overcome the limitations of signature and machine learning based detection methods and to more accurately analyze and detect the current Skype traffic pattern, this paper presents a comprehensive Skype traffic detection system that combines pattern, list and signature based application detection methods. The proposed system is applied to various Skype traffic collected through campus network to verify accuracy and detection rate.