Ing. Mohamed NAAS
Master's thesis
Towards Stealth VPN
Abstract:
Ensuring network traffic encryption for anonymity and interception prevention is essential. I conducted a thorough survey of VPN-related datasets and machine-learning approaches. I created a dataset USBVPN2022 for data scientists working on encrypted network traffic and VPN classification. The experiments in the thesis revealed key features and led to multiple proposals to achieve a stealth VPN.Abstract:
Ensuring network traffic encryption for anonymity and interception prevention is essential. I conducted a thorough survey of VPN-related datasets and machine-learning approaches. I created a dataset USBVPN2022 for data scientists working on encrypted network traffic and VPN classification. The experiments in the thesis revealed key features and led to multiple proposals to achieve a stealth VPN.
Language used: English
Date on which the thesis was submitted / produced: 8. 2. 2024
Thesis defence
- Supervisor: Ing. Jan Fesl, Ph.D.
Citation record
The right form of listing the thesis as a source quoted
NAAS, Mohamed. Towards Stealth VPN. České Budějovice, 2024. diplomová práce (Mgr.). JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH. Přírodovědecká fakulta
Full text of thesis
Contents of on-line thesis archive
Published in Theses:- Soubory jsou nedostupné do 8. 2. 2027
- Po tomto datu bude práce dostupná: světu
Other ways of accessing the text
Institution archiving the thesis and making it accessible: JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH, Přírodovědecká fakulta
UNIVERSITY OF SOUTH BOHEMIA IN ČESKÉ BUDĚJOVICE
Faculty of ScienceMaster programme / field:
Artificial Intelligence and Data Science / Artificial Intelligence and Data Science