In multi-classifier decision fusion, there is great warp when using limited training data to estimate the probability parameters of classifier. For dealing with this problem, a multi-classifier decision fusion method based on D-S (Dempster-Shafer) Evidential Reasoning (ER) was presented. The method utilized the advantages of D-S theory to describe uncertainty of classifiers. To solve the paradox problem in high conflict circumstance among multiple classifiers, a reliability weighted fusion algorithm was proposed to realize the traffic identification decision fusion. The experimental results show that the accuracy rate of majority voting and Bayes maximum posteriori probability are 78.3% and 81.7% respectively, while the proposed algorithm can improve the accuracy rate up to 82.2%-91.6%, and remain the reject rate between 4.1% and 6.2%.

Considering the complexity and poor real-time quality of classification algorithms based on the statistical characteristics of network traffic, a new traffic identification method was proposed based on transport-layer topology. According to the different host connection characteristics in terms of application types at aggregation point, the proposed method extracted topological characteristics of application types by capturing the transport layer connection information, and then produced application type pools based on Deep-in Packet Inspection (DPI) technique, finally identified the application types of traffic combining the pools and heuristic rules. The experimental results show that the proposed method gains precision higher than 85% for identifying main application types and reduces ratio of un-identified flows from 18% to 7%. It utilizes transport-layer topology information of different application types and can enhance the recognition accuracy of application types.