Summary of Enhancing Encrypted Internet Traffic Classification Through Advanced Data Augmentation Techniques, by Yehonatan Zion et al.
Enhancing Encrypted Internet Traffic Classification Through Advanced Data Augmentation Techniques
by Yehonatan Zion, Porat Aharon, Ran Dubin, Amit Dvir, Chen Hajaj
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses the challenge of classifying encrypted internet traffic by proposing two data augmentation techniques: Average and MTU. These methods aim to improve model performance and mitigate limitations of existing datasets. Experiments on three datasets demonstrate the effectiveness of these approaches in enhancing model accuracy for tasks such as video streaming and file downloading. The findings highlight the potential of data augmentation in addressing challenges in internet traffic classification, ultimately improving user Quality of Experience (QoE) and Quality of Service (QoS). Key contributions include the introduction of Average and MTU augmentations and their application to encrypted traffic classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to better classify internet traffic, even when it’s encrypted. The authors came up with two new ways to generate more data: Average augmentation and MTU augmentation. They tested these methods on different datasets and found that they work really well. This is important because it can help improve the quality of services like YouTube or Google Docs, making them faster and more reliable. |
Keywords
» Artificial intelligence » Classification » Data augmentation
