The DOI matches its Crossref record; 30 of the 32 references it cites were found in a registry or an index.
Machine learning based encrypted traffic classification: Identifying SSH and Skype
Abstract
The objective of this work is to assess the robustness of machine learning based traffic classification for classifying encrypted traffic where SSH and Skype are taken as good representatives of encrypted traffic. Here what we mean by robustness is that the classifiers are trained on data from one network but tested on data from an entirely different network. To this end, five learning algorithms — AdaBoost, Support Vector Machine, Naïe Bayesian, RIPPER and C4.5 — are evaluated using flow based features, where IP addresses, source/destination ports and payload information are not employed. Results indicate the C4.5 based approach performs much better than other algorithms on the identification of both SSH and Skype traffic on totally different networks.
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In the citing paper
“Hence, it generalizes well from one network to another.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [17]
In the citing paper
“For SSH traffic, it achieves 95.9% DR and 2.8% FPR on the Dalhousie dataset, 97.2% DR and 0.8% FPR on the AMP dataset, and 82.9% DR and 0.5% FPR on the MAWI dataset.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [17]
In the citing paper
“The identification of Skype traffic at the flow level is also addressed in Alshammari et al. [17] by employing supervised AdaBoost, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), SVM, NB and C4.5 DT classifiers.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [17]
In the citing paper
“In the evaluation, C4.5 DT outperform the other classifiers for the majority of datasets.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [17]
In the citing paper
“The C4.5 DT classifier also performed well for Skype traffic with 98.4% DR and 7.8% FPR in the Dalhousie dataset.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [17]
In the citing paper
“Furthermore, when trained and tested across datasets (i.e.across networks), it achieves 83.7% DR and 1.5% FPR.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [17]
In the citing paper
“Unfortunately, Alshammari et al. [17] do not detect the precise applications within a secure session.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [17]
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Carela-Español, D., Castell-Uroz, I., & Barlet‐Ros, P. (2026) · IEEE International Conference on Network Softwarization (NetSoft) · doi:10.1109/netsoft70012.2026.11603553
Metadata
- Authors
- Alshammari, R., & Zincir-Heywood, A. N.
- Affiliations
- Dalhousie University (CA)
- ORCID
- Alshammari, Riyad
- Venue
- 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications
- Publisher
- IEEE
- Published
- 2009-07
- Pages
- 1-8
- OpenAlex
- W2158215699
- Cited by
- 202
- References
- 32
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 15 · 2023: 14 · 2022: 17 · 2021: 18 · 2020: 13 · 2019: 11 · 2018: 13 · 2017: 16 · 2016: 10 · 2015: 9 · 2014: 15 · 2013: 17 · 2012: 8
- FWCI
- 5.40
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA) (publishedVersion)
- Funders
- Mitacs
- 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 › Artificial Intelligence › Wireless Signal Modulation Classification
- Keywords
- encrypted traffic classification, classifier robustness, flow-based features, C4.5 decision tree, AdaBoost, support vector machine, naive Bayes, RIPPER, network traffic analysis
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