Combining communication patterns & traffic patterns to enhance mobile traffic identification performance

The bandwidth of a mobile network is limited and exhausted very fast with the huge number of mobile devices and applications. In order to manage and utilize the limited bandwidth, precise mobile application identification is required. In this work, the combination of communication patterns extracted from graphlet and traffic patterns represented by packet size distribution is studied for enhancing the performance of identifying mobile traffic. There are no privacy concerns for identifying traffic with our technique; it is also effective against the complexities of mobile traffic. The real traffic of five famous mobile applications (Facebook, Line, Skype, YouTube, and Web) is used in our evaluation. The identification performance is high (0.96) of F-measure even considering only a random 50 packets of traffic in a 3-minute duration. While identifying applications, the effect of other mixed background traffic is also studied and mitigated by filtering out short lived flows with a flow duration condition. The high identification performance is still maintained after this filtering process.