Traffic flows are set of packets transferring between a client and a server with the same set of source and destination IP and port numbers. Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Classification can be used for several purposes including policy enforcement and control or QoS management. In this paper, we introduce a novel end-to-end traffic classification method to distinguish between traffic classes including VPN traffic. Classification of VPN traffic is not trivial using traditional classification approaches due to its encrypted nature. We utilize two well-known neural networks, namely multi-layer perceptron and recurrent neural network focused on two metrics: class scores and distance from the center of the classes. Such approaches combined extraction, selection, and classification functionality into a single end-to-end system to systematically learn the non-linear relationship between input and predicted performance. Therefore, we could distinguish VPN traffics from Non-VPN traffics by rejecting the unrelated features of the VPN class. Moreover, obtain the application of Non-VPN traffics at the same time. The approach is evaluated using the general traffic dataset ISCX VPN-nonVPN and the acquired real dataset. The results of the analysis demonstrate that our proposed model fulfills the realistic project's criterion for precision.

中文翻译：

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通过拒绝使用神经网络对流量进行分类：一种对 VPN 流量进行分类的新方法

流量是一组在客户端和服务器之间传输的数据包，具有相同的源和目标 IP 以及端口号。流量分类被称为将流量流分类为应用感知类的任务，例如聊天、流媒体、VoIP 等。分类可用于多种目的，包括策略实施和控制或 QoS 管理。在本文中，我们介绍了一种新颖的端到端流量分类方法来区分包括 VPN 流量在内的流量类别。由于其加密性质，使用传统分类方法对 VPN 流量进行分类并非易事。我们利用两个众所周知的神经网络，即多层感知器和循环神经网络，专注于两个指标：类分数和与类中心的距离。这些方法将提取、选择和分类功能结合到一个端到端系统中，以系统地学习输入和预测性能之间的非线性关系。因此，我们可以通过拒绝 VPN 类的无关功能来区分 VPN 流量和非 VPN 流量。并且同时获取Non-VPN流量的应用。该方法使用通用流量数据集 ISCX VPN-nonVPN 和获取的真实数据集进行评估。分析结果表明，我们提出的模型符合现实项目的精度标准。我们可以通过拒绝 VPN 类的无关功能来区分 VPN 流量和非 VPN 流量。并且同时获取Non-VPN流量的应用。该方法使用通用流量数据集 ISCX VPN-nonVPN 和获取的真实数据集进行评估。分析结果表明，我们提出的模型符合现实项目的精度标准。我们可以通过拒绝 VPN 类的无关功能来区分 VPN 流量和非 VPN 流量。并且同时获取Non-VPN流量的应用。该方法使用通用流量数据集 ISCX VPN-nonVPN 和获取的真实数据集进行评估。分析结果表明，我们提出的模型符合现实项目的精度标准。