research-article

A few shots traffic classification with mini-FlowPic augmentations

Authors:

Yuval ShavittAuthors Info & Claims

IMC '22: Proceedings of the 22nd ACM Internet Measurement Conference

Pages 647 - 654

https://doi.org/10.1145/3517745.3561436

Published: 25 October 2022 Publication History

Abstract

Internet traffic classification has been intensively studied over the past decade due to its importance for traffic engineering and cyber security. One of the best solutions to several traffic classification problems is the FlowPic approach, where histograms of packet sizes in consecutive time slices are transformed into a picture that is fed into a Convolution Neural Network (CNN) model for classification.

However, CNNs (and the FlowPic approach included) require a relatively large labeled flow dataset, which is not always easy to obtain. In this paper, we show that we can overcome this obstacle by replacing the large labeled dataset with a few samples of each class and by using augmentations in order to inflate the number of training samples. We show that common picture augmentation techniques can help, but accuracy improves further when introducing augmentation techniques that mimic network behavior such as changes in the RTT.

Finally, we show that we can replace the large FlowPics suggested in the past with much smaller mini-FlowPics and achieve two advantages: improved model performance and easier engineering. Interestingly, this even improves accuracy in some cases.

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References

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Cited By

Horowicz EShapira TShavitt Y(2024)Self-Supervised Traffic Classification: Flow Embedding and Few-Shot SolutionsIEEE Transactions on Network and Service Management10.1109/TNSM.2024.336684821:3(3054-3067)Online publication date: Jun-2024
https://doi.org/10.1109/TNSM.2024.3366848
Huang KZhou MZhang PQin XHu X(2024)Enhancing VPN Traffic Recognition Through CatBoost Feature Extraction and Stacking Ensemble LearningICC 2024 - IEEE International Conference on Communications10.1109/ICC51166.2024.10622256(79-84)Online publication date: 9-Jun-2024
https://doi.org/10.1109/ICC51166.2024.10622256
Gioacchini LSantos WLopes BDrago IMellia MAlmeida JGonçalves M(2024)Explainable Stacking Models based on Complementary Traffic Embeddings2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)10.1109/EuroSPW61312.2024.00035(261-272)Online publication date: 8-Jul-2024
https://doi.org/10.1109/EuroSPW61312.2024.00035
Show More Cited By

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cover image ACM Conferences

IMC '22: Proceedings of the 22nd ACM Internet Measurement Conference

October 2022

796 pages

ISBN:9781450392594

DOI:10.1145/3517745

General Chairs:
Chadi Barakat
Inria, Université Côte d'Azur
,
Cristel Pelsser
University of Strasbourg
,
Program Chairs:
Theophilus A. Benson
Brown University
,
David Choffnes
Northeastern University

Copyright © 2022 ACM.

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Published: 25 October 2022

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IMC '22: ACM Internet Measurement Conference

October 25 - 27, 2022

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Overall Acceptance Rate 277 of 1,083 submissions, 26%

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Cited By

Horowicz EShapira TShavitt Y(2024)Self-Supervised Traffic Classification: Flow Embedding and Few-Shot SolutionsIEEE Transactions on Network and Service Management10.1109/TNSM.2024.336684821:3(3054-3067)Online publication date: Jun-2024
https://doi.org/10.1109/TNSM.2024.3366848
Huang KZhou MZhang PQin XHu X(2024)Enhancing VPN Traffic Recognition Through CatBoost Feature Extraction and Stacking Ensemble LearningICC 2024 - IEEE International Conference on Communications10.1109/ICC51166.2024.10622256(79-84)Online publication date: 9-Jun-2024
https://doi.org/10.1109/ICC51166.2024.10622256
Gioacchini LSantos WLopes BDrago IMellia MAlmeida JGonçalves M(2024)Explainable Stacking Models based on Complementary Traffic Embeddings2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)10.1109/EuroSPW61312.2024.00035(261-272)Online publication date: 8-Jul-2024
https://doi.org/10.1109/EuroSPW61312.2024.00035
Hajaj CAharon PDubin RDvir A(2024)The art of time-bending: Data augmentation and early prediction for efficient traffic classificationExpert Systems with Applications10.1016/j.eswa.2024.124166252(124166)Online publication date: Oct-2024
https://doi.org/10.1016/j.eswa.2024.124166
Ma YLi ZXue HChang J(2024)A balanced supervised contrastive learning-based method for encrypted network traffic classificationComputers & Security10.1016/j.cose.2024.104023145(104023)Online publication date: Oct-2024
https://doi.org/10.1016/j.cose.2024.104023
Guarino IWang CFinamore APescapè ARossi D(2023)Many or Few Samples?: Comparing Transfer, Contrastive and Meta-Learning in Encrypted Traffic Classification2023 7th Network Traffic Measurement and Analysis Conference (TMA)10.23919/TMA58422.2023.10198965(1-10)Online publication date: 26-Jun-2023
https://doi.org/10.23919/TMA58422.2023.10198965
Akbari ETahmid SMalekghaini NSalahuddin MLimam NBoutaba RMathieu BMoteau STuffin S(2023)A Critical Study of Few-Shot Learning for Encrypted Traffic Classification2023 19th International Conference on Network and Service Management (CNSM)10.23919/CNSM59352.2023.10327851(1-9)Online publication date: 30-Oct-2023
https://doi.org/10.23919/CNSM59352.2023.10327851
Finamore AWang CKrolikowski JNavarro JChen FRossi DMontpetit MLeivadeas AUhlig SJaved M(2023)Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input RepresentationProceedings of the 2023 ACM on Internet Measurement Conference10.1145/3618257.3624820(36-51)Online publication date: 24-Oct-2023
https://dl.acm.org/doi/10.1145/3618257.3624820

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