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Rosetta: Enabling Robust TLS Encrypted Traffic Classification in Diverse Network Environments with TCP-Aware Traffic Augmentation

Authors:

Kun Sun,

Qi Li,

Licheng Shen,

Menghao ZhangAuthors Info & Claims

ACM TURC '23: Proceedings of the ACM Turing Award Celebration Conference - China 2023

Pages 131 - 132

https://doi.org/10.1145/3603165.3607437

Published: 25 September 2023 Publication History

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Abstract

As the majority of Internet traffic is encrypted by the Transport Layer Security (TLS) protocol, recent advances leverage Deep Learning (DL) models to conduct encrypted traffic classification. We propose Rosetta to enable robust TLS encrypted traffic classification for existing DL models. It leverages TCP-aware traffic augmentation mechanisms and self-supervised learning to understand implicit TCP semantics, and hence extracts robust features of TLS flows. Extensive experiments show that Rosetta can significantly improve the classification performance of existing DL models on TLS traffic in diverse network environments.

References

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Meng Shen, Yiting Liu, Liehuang Zhu, Xiaojiang Du, and Jiankun Hu. 2020. Fine-grained webpage fingerprinting using only packet length information of encrypted traffic. IEEE Transactions on Information Forensics and Security 16 (2020), 2046–2059.

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Payap Sirinam, Mohsen Imani, Marc Juarez, and Matthew Wright. [n. d.]. Deep fingerprinting: Undermining website fingerprinting defenses with deep learning. In CCS 18.

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Cited By

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Chen YHou BWu BHu H(2025)CD-Net: Robust mobile traffic classification against apps updatingComputers & Security10.1016/j.cose.2024.104214150(104214)Online publication date: Mar-2025
https://doi.org/10.1016/j.cose.2024.104214
Liu TMa XLiu LLiu XZhao YHu NGhafoor K(2024)LAMBERT: Leveraging Attention Mechanisms to Improve the BERT Fine-Tuning Model for Encrypted Traffic ClassificationMathematics10.3390/math1211162412:11(1624)Online publication date: 22-May-2024
https://doi.org/10.3390/math12111624
Zhou JFu WHu WSun ZHe TZhang Z(2024)Challenges and Advances in Analyzing TLS 1.3-Encrypted Traffic: A Comprehensive SurveyElectronics10.3390/electronics1320400013:20(4000)Online publication date: 11-Oct-2024
https://doi.org/10.3390/electronics13204000
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Published In

ACM TURC '23: Proceedings of the ACM Turing Award Celebration Conference - China 2023

July 2023

173 pages

ISBN:9798400702334

DOI:10.1145/3603165

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 25 September 2023

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ACM TURC '23: ACM Turing Award Celebration Conference 2023

July 28 - 30, 2023

Wuhan, China

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Chen YHou BWu BHu H(2025)CD-Net: Robust mobile traffic classification against apps updatingComputers & Security10.1016/j.cose.2024.104214150(104214)Online publication date: Mar-2025
https://doi.org/10.1016/j.cose.2024.104214
Liu TMa XLiu LLiu XZhao YHu NGhafoor K(2024)LAMBERT: Leveraging Attention Mechanisms to Improve the BERT Fine-Tuning Model for Encrypted Traffic ClassificationMathematics10.3390/math1211162412:11(1624)Online publication date: 22-May-2024
https://doi.org/10.3390/math12111624
Zhou JFu WHu WSun ZHe TZhang Z(2024)Challenges and Advances in Analyzing TLS 1.3-Encrypted Traffic: A Comprehensive SurveyElectronics10.3390/electronics1320400013:20(4000)Online publication date: 11-Oct-2024
https://doi.org/10.3390/electronics13204000
Jansen RWails RJohnson AAyday EVaidya J(2024)Repositioning Real-World Website Fingerprinting on TorProceedings of the 23rd Workshop on Privacy in the Electronic Society10.1145/3689943.3695047(124-140)Online publication date: 20-Nov-2024
https://dl.acm.org/doi/10.1145/3689943.3695047
Cebere BRossow C(2024)Understanding Web Fingerprinting with a Protocol-Centric ApproachProceedings of the 27th International Symposium on Research in Attacks, Intrusions and Defenses10.1145/3678890.3678910(17-34)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3678890.3678910
Wang CYin JLi ZXu HZhang ZLiu QChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Identifying VPN Servers through Graph-Represented BehaviorsProceedings of the ACM Web Conference 202410.1145/3589334.3645552(1790-1799)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645552
Han XCui SQin JLiu SJiang BDong CLu ZLiu BChua TNgo CKa-Wei Lee RKumar RLauw H(2024)ContraMTD: An Unsupervised Malicious Network Traffic Detection Method based on Contrastive LearningProceedings of the ACM Web Conference 202410.1145/3589334.3645479(1680-1689)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645479
Xie RCao JZhu YZhang YHe YPeng HWang YXu MSun KDong ELi QZhang MLi J(2024)Cactus: Obfuscating Bidirectional Encrypted TCP Traffic at Client SideIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.344253019(7659-7673)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3442530
Anser OFrançois JChrisment I(2024)Automated Machine Learning Configuration to Learn Intrusion Detectors on Attack-Free Datasets2024 IEEE 49th Conference on Local Computer Networks (LCN)10.1109/LCN60385.2024.10639690(1-7)Online publication date: 8-Oct-2024
https://doi.org/10.1109/LCN60385.2024.10639690
Luo ZLi YTan SHe D(2024)Enhancing Flow Embedding Through Trace: A Novel Self-supervised Approach for Encrypted Traffic Classification2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651222(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651222
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Observing real Multipath TCP traffic

Encrypted TLS Traffic Classification on Cloud Platforms

Achieving bounded fairness for multicast and TCP traffic in the Internet

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