Enhancing the Performance of Mobile Traffic Identification with Communication Patterns

Traffic classification is important especially for managing and monitoring networks which contain a wide variety of traffic, such as in the mobile network. Using only packet based feature in traditional classification is not enough for classifying mobile application traffic because of the complexity of mobile traffic. Therefore, this study proposes the technique that combines the packet size distribution and communication patterns extracted via graph let for identifying mobile application. The technique is robust to the complexity of mobile traffic and has no privacy concerns. Validation results over five popular mobile applications (Facebook, Line, Skype, You Tube, and Web) demonstrate that our combined method achieves high performance (0.95) of F-measure even using only randomly sampled 50 packets during 3-minute time interval. Moreover, the combination of these features distinguishes various applications with similar characteristics such as Facebook and Web.