Summary of Data Augmentation For Traffic Classification, by Chao Wang et al.
Data Augmentation for Traffic Classification
by Chao Wang, Alessandro Finamore, Pietro Michiardi, Massimo Gallo, Dario Rossi
First submitted to arxiv on: 19 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Networking and Internet Architecture (cs.NI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper bridges a gap in Data Augmentation (DA) applications by exploring its potential in Traffic Classification (TC) tasks. The authors benchmark 18 augmentation functions on three TC datasets, using packet time series as input representation and various training conditions. The results show that DA can improve model performance, with certain augmentations being more effective than others. By analyzing the latent space of basic models, the study sheds light on how different augmentations impact classification performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers are trying to make a special kind of artificial intelligence better at recognizing types of traffic. They test 18 ways to add fake data to help train the AI model. The results show that adding fake data can actually make the AI better at guessing what type of traffic it is looking at. The study also helps us understand why some fake data works better than others. |
Keywords
* Artificial intelligence * Classification * Data augmentation * Latent space * Time series
