The persistent emergence of new network applications, along with encrypted network communication, has make traffic analysis become a challenging issue in network management and cyberspace security. Currently, virtual private network (VPNs) has become one of the most popular encrypted communication services for bypassing censorship and guarantee remote access to geographically locked services. In this paper, a novel identification scheme of VoIP traffic tunneled through VPN is proposed. We employed a set of Flow Spatio-Temporal Features (FSTF) to six well-known classifiers, including decision trees, K-Nearest Neighbor (KNN), Bagging and Boosting via C4.5, and Multi-Layer perceptron (MLP). The overall accuracy, precision, sensitivity, and F-measure verify that the proposed scheme can effectively distinguish between the VoIP flows and Non-VoIP ones in VPN traffic.

中文翻译：

---

通过流时空特征识别VPN隧道中的VoIP流量

新网络应用程序的不断涌现以及加密的网络通信已使流量分析成为网络管理和网络空间安全中的一个具有挑战性的问题。当前，虚拟专用网络（VPN）已成为最流行的加密通信服务之一，用于绕过审查并保证对地理锁定服务的远程访问。本文提出了一种通过VPN隧道传输的VoIP流量的新的识别方案。我们对六个著名的分类器采用了一套流时空时空特征（FSTF），包括决策树，K最近邻（KNN），通过C4.5进行装袋和增强以及多层感知器（MLP）。整体精度，精密度，灵敏度，