A comprehensive machine learning-based approach for virtual private network traffic detection, classification and hiding

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12220%2F25%3A43910903" target="_blank" >RIV/60076658:12220/25:43910903 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21240/25:00386158 RIV/60076658:12310/25:43910903
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S1389128625004979" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1389128625004979</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.comnet.2025.111530" target="_blank" >10.1016/j.comnet.2025.111530</a>

Result language
angličtina
Original language name
A comprehensive machine learning-based approach for virtual private network traffic detection, classification and hiding
Original language description
Virtual private networks (VPNs) are often used today for remote access to corporate networks or to access information resources limited to specific IP ranges or specific geolocations. Reliable detection and classification of normal or encrypted VPN traffic is a non-trivial task that has not yet been reliably solved. In our research, we created a large dataset containing samples of network traffic of different VPN protocols. We used the dataset to build nine machine learning (ML) models and compared their efficiency. Our best ML models can detect VPN network traffic with very high accuracy, subsequently classify the type of VPN protocol, and evaluate the content of traffic transported via the encrypted VPN protocols. To validate the robustness of our models, we invented and applied various VPN traffic detection obfuscation methods whose usage may interfere with network traffic identification and classification. Such methods can also be used to design and implement more secure next-generation VPN protocols that will be potentially not detectable by methods based on ML models.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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