  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).
