Classification of network traffic recently has attracted a great deal of interest as it plays important roles in many areas such as traffic engineering, service class mapping, network management etc. One of the challenging issues for existing detection schemes is that they need prior manual analysis to detect unknown traffic, which is infeasible to cope with the fast growing number of new applications. In this paper, we propose an automatic traffic classification scheme, which is realised by managing traffic detection knowledge with the use of ontologies on the one hand, while developing the self-learning model on traffic detection according to ontologies on the other hand. Also, based on two scenarios, the experiment results demonstrate the automated detection capability for the proposed scheme.