TSCRNN: A novel classification scheme of encrypted traffic based on flow spatiotemporal features for efficient management of IIoT
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TSCRNN enriches flows by random sampling to train the model.
“TSCRNN enriches flows by random sampling to train the model”
From ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification (2022) · cites this paper as [21]
Existing deep learning methods for encrypted traffic classification highly rely on the amount and distribution of labelled training data, which causes model bias and poor adaptation to newly emerged encryption.
“However, these methods highly rely on the amount and distribution of labelled training data, which is easy to cause model bias and hard to adapt to newly emerged encryption.”
From ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification (2022) · cites this paper as [21]
Cites
End-to-end encrypted traffic classification with one-dimensional convolution neural networks
Wang, W., Zhu, M., Wang, J., Zeng, X., & Yang, Z. (2017) · 2017 IEEE International Conference on Intelligence and Security Informatics (ISI) · doi:10.1109/isi.2017.8004872 · Cited by 991 · see citing works
Xie, J., Yu, F. R., Huang, T., Xie, R., Liu, J., Wang, C., & Liu, Y. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2866942 · Cited by 700 · see citing works
Robust Network Traffic Classification
Zhang, J., Chen, X., Xiang, Y., Zhou, W., & Wu, J. (2015) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2014.2320577 · Cited by 391 · see citing works
An efficient instance selection algorithm to reconstruct training set for support vector machine
Liu, C., Wang, W., Wang, M., Lv, F., & Konan, M. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.10.031 · Cited by 146 · see citing works
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Lin, X., Xiong, G., Gou, G., Li, Z., Shi, J., & Yu, J. (2022) · Proceedings of the ACM Web Conference 2022 · doi:10.1145/3485447.3512217 · Cited by 599 · see citing works
Internet of Things (IoT) security with blockchain technology: A state-of-the-art review
Ayub Khan, A., Laghari, A. A., Shaikh, Z. A., Dacko-Pikiewicz, Z., & Kot, S. (2022) · IEEE Access · doi:10.1109/access.2022.3223370 · Cited by 223 · see citing works
MT-FBERT: Malicious Traffic Detection Based on Efficient Federated Learning of BERT
Tang, J., Huang, Z., & Li, C. (2025) · Future Internet · doi:10.3390/fi17080323 · Cited by 6 · see citing works
- Authors
- Lin, K., Xu, X., & Gao, H.
- Affiliations
- Nanjing University of Posts and Telecommunications (CN)
- Shanghai University (CN)
- Corresponding author
- Xu, Xiaolong
- Gao, Honghao
- Venue
- Computer Networks
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 190
- Pages
- 107974
- ISSN
- 1389-1286 (ISSN-L)
- OpenAlex
- W3134226201
- Cited by
- 226
- References
- 4
- Citations by year
- 2026: 54 · 2025: 66 · 2024: 55 · 2023: 28 · 2022: 20 · 2021: 3
- FWCI
- 9.36
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computer Networks (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Keywords
- encrypted traffic classification, Industrial Internet of Things, spatio-temporal features, bidirectional LSTM, CNN feature extraction, traffic sampling, network traffic characterization, 5G
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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