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Detection of DNS over HTTPS Tunneling using Random Forest Supervised Learning

Pednekar, Tejaswi Sharad (2022) Detection of DNS over HTTPS Tunneling using Random Forest Supervised Learning. Masters thesis, Dublin, National College of Ireland.

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As a network protocol, Domain Name System (DNS) is prone to a number of security flaws. For address some of DNS's weaknesses, a new protocol called DNS over HTTPS (DoH) is being developed to increase privacy and guard against certain persistent assaults. To avoid eavesdropping and man-in-the-middle attacks, the DoH protocol encrypts DNS requests for the DoH client and sends them over a tunnel. This study paper thoroughly investigates these security flaws, offers a taxonomy of probable DNS attacks, examines the security features of the DoH protocol, and categorises DNS attacks applicable to DoH. I simulated DoH tunnels to attain these goals.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Machine Learning; Random Forest; DOH; Tunneling
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software > Computer Security
T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Cyber Security
Depositing User: Tamara Malone
Date Deposited: 29 Dec 2022 11:55
Last Modified: 07 Mar 2023 12:36

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