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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68407700:21240/25:00386158 RIV/60076658:12310/25:43910903

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Computer Networks

ISSN

1389-1286

e-ISSN

1872-7069

Svazek periodika

neuveden

Číslo periodika v rámci svazku

October

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

15

Strana od-do

1-15

Kód UT WoS článku

001578007500001

EID výsledku v databázi Scopus

2-s2.0-105011078357