Characterization of Encrypted and VPN Traffic using Time-related Features
Open accessAbstract
Traffic characterization is one of the major challenges in today’s security industry. The continuous evolution and generation of new applications and services, together with the expansion of encrypted communications makes it a difficult task. Virtual Private Networks (VPNs) are an example of encrypted communication service that is becoming popular, as method for bypassing censorship as well as accessing services that are geographically locked. In this paper, we study the effectiveness of flow-based time-related features to detect VPN traffic and to characterize encrypted traffic into different categories, according to the type of traffic e.g., browsing, streaming, etc. We use two different well-known machine learning techniques (C4.5 and KNN) to test the accuracy of our features. Our results show high accuracy and performance, confirming that time-related features are good classifiers for encrypted traffic characterization.
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Cited by 1,213
- Authors
- Draper-Gil, G., Lashkari, A. H., Mamun, M. S. I., & A. Ghorbani, A.
- Affiliations
- University of New Brunswick (CA)
- University of New Brunswick, Canada
- Venue
- Proceedings of the 2nd International Conference on Information Systems Security and Privacy
- Published
- 2016
- Pages
- 407-414
- OpenAlex
- W2343828539
- Cited by
- 1,213
- References
- 0
- Citations by year
- 2026: 155 · 2025: 209 · 2024: 216 · 2023: 175 · 2022: 157 · 2021: 139 · 2020: 76 · 2019: 51 · 2018: 26 · 2017: 8 · 2015: 1
- FWCI
- 15.27
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- International Conference on Information Systems Security and Privacy (ICISSP) (publishedVersion)
- International Conference on Information Systems Security and Privacy (ICISSP) (publishedVersion, cc-by-nc-nd)
- 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 Vision and Pattern Recognition › Image and Video Quality Assessment
- Keywords
- virtual private networks, network flow features, network traffic classification, streaming traffic, C4.5 decision tree, k-nearest neighbors
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