TrafficGCN: Mobile Application Encrypted Traffic Classification Based on GCN

Hongbo Xu, Shuhao Li, Zhenyu Cheng, Rui Qin, Jiang Xie, Peishuai Sun

Abstract: With the gradual adoption of 4G and 5G communication technologies, the number of mobile devices has increased dramatically. Identifying apps can provide technical support for fine-grained network management or optimizing the quality of network connections. The current development of new technologies, such as HTTPS and content delivery networks (CDN) technology, presents new challenges to mobile application classification. Existing techniques either ignore the implicit graph relationships in the traffic or lack comprehensive traffic features analysis, resulting in poor classification accuracy or inapplicability to large-scale data. In this paper, we propose TrafficGCN, a novel mobile application classification technique to solve the above problem. TrafficGCN constructs communication behavior graphs by combining packet-level and flow-level traffic data. The graph convolutional neural network (GCN) is then used to learn a large number of graph connectivity relations and node properties generated by different applications. In addition, we present a traffic graph dataset for mobile application classification. Comparing TrafficGCN with traditional deep learning algorithms (DNN, CNN, LSTM) and the recently developed techniques (MAppGraph, FlowPrint, AppScanner), the experimental results show that it significantly improves classification performance 5.44%-18.72% in various metrics. These results demonstrate that TrafficGCN has great potential for mobile network management,