@inproceedings{6c8aad13b36e44af9ebfe9516dcbd13c,
title = "Rosetta: Enabling Robust TLS Encrypted Traffic Classification in Diverse Network Environments with TCP-Aware Traffic Augmentation",
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.",
author = "Renjie Xie and Yixiao Wang and Jiahao Cao and Enhuan Dong and Mingwei Xu and Kun Sun and Qi Li and Licheng Shen and Menghao Zhang",
note = "Publisher Copyright: {\textcopyright} 2023 Owner/Author.; 2023 ACM Turing Award Celebration Conference, CHINA 2023 ; Conference date: 28-07-2023 Through 30-07-2023",
year = "2023",
month = jul,
day = "28",
doi = "10.1145/3603165.3607437",
language = "英语",
series = "Proceedings of ACM Turing Award Celebration Conference, CHINA 2023",
publisher = "Association for Computing Machinery, Inc",
pages = "131--132",
booktitle = "Proceedings of ACM Turing Award Celebration Conference, CHINA 2023",
}