Existing website fingerprinting techniques are not effective with video streaming traffic when the encrypted traffic contains multiple streams. This paper presents a deep learning-based source identification method for identifying multiple video sources within a single encrypted tunnel. The core contribution is a novel feature inspired by natural language processing (NLP) that allows existing NLP techniques to identify the source. The feature extraction method is described. A large dataset containing video streaming and web traffic is created to verify its effectiveness. Results are obtained by applying several NLP methods to show that the proposed method performs well on both binary and multilabel traffic classification problems. The work proves that the method can overcome the challenges given by mixed-traffic tunnels.

中文翻译：

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一种基于深度神经网络的自然语言多标签视频流源识别方法

当加密流量包含多个流时，现有的网站指纹识别技术对视频流流量无效。本文提出了一种基于深度学习的源识别方法，用于识别单个加密隧道内的多个视频源。核心贡献是一个受自然语言处理 (NLP) 启发的新功能，它允许现有的 NLP 技术识别来源。描述了特征提取方法。创建包含视频流和网络流量的大型数据集以验证其有效性。通过应用几种 NLP 方法获得的结果表明，所提出的方法在二元和多标签流量分类问题上都表现良好。工作证明该方法可以克服混合交通隧道带来的挑战。