Publication detail
Comparative Analysis of DNS over HTTPS Detectors
JEŘÁBEK, K. HYNEK, K. RYŠAVÝ, O.
Original Title
Comparative Analysis of DNS over HTTPS Detectors
Type
journal article in Web of Science
Language
English
Original Abstract
DNS over HTTPS (DoH) is a protocol that encrypts DNS traffic to improve user privacy and security. However, its use also poses challenges for network operators and security analysts who need to detect and monitor network traffic for security purposes. Therefore, there are multiple DoH detection proposals that leverage machine learning to identify DoH connections; however, these proposals were often tested on different datasets, and their evaluation methodologies were not consistent enough to allow direct performance comparison. In this study, seven DoH detection proposals were recreated and evaluated with six different experiments to answer research questions that targeted specific deployment scenarios concerning ML-model transferability, usability, and longevity. For thorough testing, a large Collection of DoH datasets along with a novel 5-week dataset was used, which enabled the evaluation of models’ longevity. This study provides insights into the current state of DoH detection techniques and evaluates the models in scenarios that have not been previously tested. Therefore, this paper goes beyond classical replication studies and shows previously unknown properties of seven published DoH detectors.
Keywords
DNS over HTTPS,DoH, detection,comparative analysis,machine learning,network security
Authors
JEŘÁBEK, K.; HYNEK, K.; RYŠAVÝ, O.
Released
20. 4. 2024
Publisher
Elsevier
ISBN
1872-7069
Periodical
Computer Networks
Year of study
247
Number
June
State
Kingdom of the Netherlands
Pages from
1
Pages to
13
Pages count
13
URL
Full text in the Digital Library
BibTex
@article{BUT188647,
author="Kamil {Jeřábek} and Karel {Hynek} and Ondřej {Ryšavý}",
title="Comparative Analysis of DNS over HTTPS Detectors",
journal="Computer Networks",
year="2024",
volume="247",
number="June",
pages="1--13",
doi="10.1016/j.comnet.2024.110452",
issn="1872-7069",
url="https://doi.org/10.1016/j.comnet.2024.110452"
}