Classification of Virtual Private networks encrypted traffic using ensemble learning algorithms

Almomani, A (2022) Classification of Virtual Private networks encrypted traffic using ensemble learning algorithms. Egyptian Informatics Journal. ISSN 1110-8665

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Virtual Private Networks (VPNs) are one example of encrypted communication services commonly used to bypass censorship and access geographically locked services. This study performed VPN and non-VPN traffic analysis and developed a classification system based on the new techniques of machine learning classifiers known as stacking ensemble learning. The methods used for VPN and Non-VPN classification use three machine learning techniques: random forest, neural network, and support vector machine. To assess the proposed method's performance, we tested it on a dataset containing 61 features. The experiment results accurately prove the study's classifiers to differentiate between VPN and Non-VPN traffic. The accuracy level was approximately 99% in the training and testing phase. The study's classifiers also show the best standard deviation, with a 100% accuracy rate compared to other A.I. classifier methods.

Affiliation: Skyline University College
SUC Author(s): Almomani, A ORCID:
All Author(s): Almomani, A
Item Type: Article
Uncontrolled Keywords: Ensemble Learning, Machine learning, (VPN (and Non-VPN traffic analysis, Encrypted traffic
Subjects: B Information Technology > BR Deep Learning
Divisions: Skyline University College > School of IT
Depositing User: Mr Veeramani Rasu
Date Deposited: 09 Aug 2022 06:23
Last Modified: 09 Aug 2022 06:23
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