With the rapid development of mobile Internet, the number of mobile devices has surged to a record high. Recognizing and analyzing mobile traffic from a large number of mixed traffic is the first step to study the characteristics of mobile Internet. It can also provide valuable information for mobile network measurement and management, mobile security and privacy protection. This paper summarizes the common methods of network traffic identification, and proposes a mobile traffic identification method based on multidimensional statistical characteristics of data flow. This method extracts the representative features of data stream from three aspects: hardware features, operating system fingerprints and user usage habits, and analyses the features. An ensemble learning method is used to generate the recognition model. The accuracy of mobile traffic identification and five mainstream operation classification results are more than 99%. Compared with the UAFs method mentioned in this paper, the accuracy is improved by about 8%. The features extracted by this method are multidimensional and have practical significance. The features integrate the data flow characteristics network layer and transport layer. Compared with the method using deep packet inspection detection, this method is suitable for the classification of encrypted traffic.
