Towards Stealth VPN

Ing. Mohamed NAAS

Diplomová práce

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.

Keywords

USBVPN2022, network traffic classification, machine Learning, virtual private networks, adversarial attacks

Jazyk práce: angličtina

Datum vytvoření / odevzdání či podání práce: 8. 2. 2024

Obhajoba závěrečné práce

• Vedoucí: Ing. Jan Fesl, Ph.D.

JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH

Přírodovědecká fakulta

Magisterský studijní program / obor:
Artificial Intelligence and Data Science / Artificial Intelligence and Data Science