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

TL;DR

This paper introduces a deep neural network approach for multi-label video streaming traffic classification within encrypted tunnels, utilizing NLP-inspired features to improve accuracy and enable zero-shot learning.

Contribution

It proposes a novel NLP-inspired feature extraction method for traffic classification and demonstrates its effectiveness on a large dataset with multi-label and zero-shot capabilities.

Findings

High accuracy in binary and multi-label classification

Effective zero-shot learning performance

NLP-inspired features enhance traffic identification

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.