Enhancing Encrypted Internet Traffic Classification Through Advanced Data Augmentation Techniques

TL;DR

This paper introduces two novel data augmentation techniques, Average and MTU augmentation, to improve encrypted internet traffic classification performance, addressing data scarcity and enhancing model robustness.

Contribution

The paper presents two new data augmentation methods specifically designed for encrypted traffic classification, improving model accuracy and robustness against limited and homogeneous datasets.

Findings

Augmentation techniques significantly improve classification accuracy.

Methods enhance robustness to varying MTUs.

Results demonstrate better performance on multiple datasets.

Abstract

The increasing popularity of online services has made Internet Traffic Classification a critical field of study. However, the rapid development of internet protocols and encryption limits usable data availability. This paper addresses the challenges of classifying encrypted internet traffic, focusing on the scarcity of open-source datasets and limitations of existing ones. We propose two Data Augmentation (DA) techniques to synthetically generate data based on real samples: Average augmentation and MTU augmentation. Both augmentations are aimed to improve the performance of the classifier, each from a different perspective: The Average augmentation aims to increase dataset size by generating new synthetic samples, while the MTU augmentation enhances classifier robustness to varying Maximum Transmission Units (MTUs). Our experiments, conducted on two well-known academic datasets and a commercial dataset, demonstrate the effectiveness of these approaches in improving model performance and mitigating constraints associated with limited and homogeneous datasets. Our findings underscore the potential of data augmentation in addressing the challenges of modern internet traffic classification. Specifically, we show that our augmentation techniques significantly enhance encrypted traffic classification models. This improvement can positively impact user Quality of Experience (QoE) by more accurately classifying traffic as video streaming (e.g., YouTube) or chat (e.g., Google Chat). Additionally, it can enhance Quality of Service (QoS) for file downloading activities (e.g., Google Docs).