A Natural Language-Inspired Multi-label Video Streaming Traffic Classification Method Based on Deep Neural Networks
This work addresses encrypted traffic classification for network management, but it is incremental as it adapts existing NLP techniques to a specific domain.
The paper tackled the problem of classifying multiple encrypted video streaming sources simultaneously by introducing a novel NLP-inspired feature, achieving strong performance on binary and multilabel classification tasks and demonstrating zero-shot learning capability.
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.