Cited by 202Internet Traffic Analysis and Secure E-votingNetwork Security and Intrusion DetectionWireless Signal Modulation Classification

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Machine learning based encrypted traffic classification: Identifying SSH and Skype

Alshammari, Riyad, Zincir-Heywood, A. Nur·2009·2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications

DOI 10.1109/cisda.2009.5356534

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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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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
DOI: 10.1109/cisda.2009.5356534
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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Machine learning based encrypted traffic classification: Identifying SSH and Skype

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