Abstract
The thesis is based on the thesis “On the Cloud-Based Network Traffic Classification and Applications Identification Services” proposed by Master Gin-Yuan-Jai in 2012. The thesis proposed by Master Gin-Yuan-Jai in 2012 proposes a machine learning-based high-accuracy algorithm called “APPlication Round method (APPR)” to identify network application traffic at the early stage. For each TCP/UDP flow, discriminators available at the early stage are determined to support high-accuracy traffic classification. But the accuracy for the real-time traffic classification is only 60%. This thesis proposes some methods for improving the accuracy and the efficacy of the traffic classification for real-time network. And this thesis adds the state machine to APPR for improving the accuracy of the traffic classification. Adding Pre-filter improves the efficacy of the traffic classification. The function of the Pre-filter is filtering the well-known applications previously. Such discriminators characterize the possible negotiation behaviors of each flow from an application layer perspective. By applying a pruned C4.5 tree machine learning algorithm to real traffic trace, this thesis proposes the method to add the dimension of the algorithm for increasing the accuracy of the traffic classification. The accuracy of the real-time campus network is maximal 91.2%, with an average overall accuracy of 87.55%. Compared to the thesis proposed by Master Gin-Yuan-Jai in 2012, the proposed methods provides more than 27% to 30% improvement of overall accuracy for the real-time campus network. Furthermore, the proposed method is also appropriate for identifying encrypted protocols and has the advantages of high accuracy and support for real-time classification.