| Autor: |
Ammar Almomani |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Egyptian Informatics Journal, Vol 23, Iss 4, Pp 57-68 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1110-8665 |
| DOI: |
10.1016/j.eij.2022.06.006 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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