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The impact of Domain Name Server (DNS) over Hypertext Transfer Protocol Secure (HTTPS) on cyber security: limitations, challenges, and detection techniques

The impact of Domain Name Server (DNS) over Hypertext Transfer Protocol Secure (HTTPS) on cyber security: limitations, challenges, and detection techniques

Dawood, Muhammad, Tu, Shanshan, Xiao, Chuangbai, Haris, Muhammad, Alasmary, Hisham, Waqas, Muhammad ORCID: 0000-0003-0814-7544 and Rehman, Sadaqat Ur (2024) The impact of Domain Name Server (DNS) over Hypertext Transfer Protocol Secure (HTTPS) on cyber security: limitations, challenges, and detection techniques. Computers, Materials and Continua, 80 (3). pp. 4513-4542. ISSN 1546-2218 (Print), 1546-2226 (Online) (doi:https://doi.org/10.32604/cmc.2024.050049)

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Abstract

The DNS over HTTPS (Hypertext Transfer Protocol Secure) (DoH) is a new technology that encrypts DNS traffic, enhancing the privacy and security of end-users. However, the adoption of DoH is still facing several research challenges, such as ensuring security, compatibility, standardization, performance, privacy, and increasing user awareness. DoH significantly impacts network security, including better end-user privacy and security, challenges for network security professionals, increasing usage of encrypted malware communication, and difficulty adapting DNS-based security measures. Therefore, it is important to understand the impact of DoH on network security and develop new privacy-preserving techniques to allow the analysis of DoH traffic without compromising user privacy. This paper provides an in-depth analysis of the effects of DoH on cybersecurity. We discuss various techniques for detecting DoH tunneling and identify essential research challenges that need to be addressed in future security studies. Overall, this paper highlights the need for continued research and development to ensure the effectiveness of DoH as a tool for improving privacy and security.

Item Type: Article
Uncontrolled Keywords: DNS, DNS over HTTPS, cybersecurity, machine learning
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified: 20 Nov 2024 12:57
URI: http://gala.gre.ac.uk/id/eprint/48666

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