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A Natural Language-Inspired Multi-label Video Streaming Traffic Classification Method Based on Deep Neural Networks

2019-06-04 00:21:21
Yan Shi, Dezhi Feng, Subir Biswas

Abstract

This paper presents a deep-learning based traffic classification method for identifying multiple streaming video sources at the same time within an encrypted tunnel. The work defines a novel feature inspired by Natural Language Processing (NLP) that allows existing NLP techniques to help the traffic classification. The feature extraction method is described, and 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. We also show the ability to achieve zero-shot learning with the proposed method.

Abstract (translated)

本文提出了一种基于深度学习的流量分类方法,用于在加密隧道内同时识别多个流视频源。这项工作定义了一个受自然语言处理(NLP)启发的新特性,该特性允许现有的NLP技术帮助进行流量分类。描述了特征提取方法,建立了一个包含视频流和网络流量的大数据集,验证了该方法的有效性。结果表明,该方法对二值和多标签流量分类问题都有很好的效果。我们还展示了该方法实现零镜头学习的能力。

URL

https://arxiv.org/abs/1906.02679

PDF

https://arxiv.org/pdf/1906.02679.pdf


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