BehavSniffer: Sniff User Behaviors from the Encrypted Traffic by Traffic Burst Graphs

With the increasing popularity of encryption pro-tocols in application and the rapid development of network applications, traffic classification has become a major challenge for mobile service providers. The failure of traditional classification methods and low classification accuracy are the problems that need to be solved urgently in traffic classification research. Therefore, we propose the scheme of BehavSniffer to sniff user behaviors from the encrypted traffic. The core idea is to propose Traffic Burst Graph (TBG) for extracting multidimensional features from bidirectional interactive data flows, and do feature fusion based on Kernel Principal Component Analysis (KPCA) and Deep Neural Network (DNN). In this way, BehavSniffer can learn both high and low-order combined structural features from traffic patterns of user behavior. Meanwhile, we propose the user behavior dataset, named WWT, from three widely used social media applications (WeChat, WhatsApp, Telegram). Experimental results show that BehavSniffer outperforms stateof-the-art methods, with AUC of 0.987 and accuracy of 99.8%, respectively.