Document Type
Article
Publication Date
6-30-2023
Publication Title
2023 Silicon Valley Cybersecurity Conference (SVCC)
Pages
1-8
Publisher Name
IEEE
Abstract
The DNS over HTTPS (DoH) protocol is implemented to improve the original DNS protocol that uses unencrypted DNS queries and responses. With the DNS traffic, an eavesdropper can easily identify websites that a user is visiting. In order to address this concern of web privacy, encryption is used by performing a DNS lookup over HTTPS. In this paper, we studied whether the encrypted DoH traffic could be exploited to identify websites that a user has visited. This is a different type of website fingerprinting by analyzing encrypted DNS network traffic rather than the network traffic between the client and the web server. DNS typically uses fewer network packets than a website download. Our model and algorithm can accurately predict one out of 10, 000 websites with a 95% accuracy using the first 50 DoH packets. In the open-world environment with 100, 000 websites, our model achieves an F1-score of 93%.
Recommended Citation
Shao, Yong; Hernandez, Kenneth; Yang, Kia; Chan-Tin, Eric; and Abuhamad, Mohammed. Lightweight and Effective Website Fingerprinting over Encrypted DNS. 2023 Silicon Valley Cybersecurity Conference (SVCC), , : 1-8, 2023. Retrieved from Loyola eCommons, Computer Science: Faculty Publications and Other Works, http://dx.doi.org/10.1109/SVCC56964.2023.10165086
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Copyright Statement
© IEEE, 2023.
Comments
Author Posting © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The definitive version of this work was published in 2023 Silicon Valley Cybersecurity Conference (SVCC), (June 30, 2023), http://doi.org/10.1109/SVCC56964.2023.10165086.