The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Markov chain fingerprinting to classify encrypted traffic
Abstract
In this paper, we propose stochastic fingerprints for application traffic flows conveyed in Secure Socket Layer/Transport Layer Security (SSL/TLS) sessions. The fingerprints are based on first-order homogeneous Markov chains for which we identify the parameters from observed training application traces. As the fingerprint parameters of chosen applications considerably differ, the method results in a very good accuracy of application discrimination and provides a possibility of detecting abnormal SSL/TLS sessions. Our analysis of the results reveals that obtaining application discrimination mainly comes from incorrect implementation practice, the misuse of the SSL/TLS protocol, various server configurations, and the application nature.
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Cites
Toward the Accurate Identification of Network Applications
Moore, A. W., & Papagiannaki, K. (2005) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-31966-5_4 · Cited by 801
Internet traffic classification demystified: myths, caveats, and the best practices
Kim, H., Claffy, K., Fomenkov, M., Barman, D., Faloutsos, M., & Lee, K. (2008) · Proceedings of the 2008 ACM CoNEXT Conference on - CONEXT '08 · doi:10.1145/1544012.1544023 · Cited by 485
Machine learning based encrypted traffic classification: Identifying SSH and Skype
Alshammari, R., & Zincir-Heywood, A. N. (2009) · 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications · doi:10.1109/cisda.2009.5356534 · Cited by 202 · cited 1×
“Some authors focus on classification of encrypted traffic [4], [21], [13], [22].”
Background
Cited by
FS-Net: A Flow Sequence Network For Encrypted Traffic Classification
Liu, C., He, L., Xiong, G., Cao, Z., & Li, Z. (2019) · IEEE INFOCOM 2019 - IEEE Conference on Computer Communications · doi:10.1109/infocom.2019.8737507 · Cited by 630
Shen, M., Zhang, J., Zhu, L., Xu, K., & Du, X. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3050608 · Cited by 392
A survey of methods for encrypted traffic classification and analysis
Velan, P., Čermák, M., Čeleda, P., & Drašar, M. (2015) · International Journal of Network Management · doi:10.1002/nem.1901 · Cited by 377
Towards QoE assessment of encrypted YouTube adaptive video streaming in mobile networks
Pan, W., Cheng, G., Wu, H., & Tang, Y. (2016) · 2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS) · doi:10.1109/iwqos.2016.7590437 · Cited by 30
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
Metadata
- Authors
- Korczynski, M., & Duda, A.
- Venue
- IEEE INFOCOM 2014 - IEEE Conference on Computer Communications
- Publisher
- IEEE
- Published
- 2014-04
- Pages
- 781-789
- OpenAlex
- W2000756828
- Cited by
- 192
- References
- 3
- FWCI
- 5.73
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Korczynski, M., & Duda, A. (2014). Markov chain fingerprinting to classify encrypted traffic. IEEE INFOCOM 2014 - IEEE Conference on Computer Communications. https://doi.org/10.1109/infocom.2014.6848005
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
