skip to main content
research-article

Attacking DoH and ECH: Does Server Name Encryption Protect Users’ Privacy?

Published:23 February 2023Publication History
Skip Abstract Section

Abstract

Privacy on the Internet has become a priority, and several efforts have been devoted to limit the leakage of personal information. Domain names, both in the TLS Client Hello and DNS traffic, are among the last pieces of information still visible to an observer in the network. The Encrypted Client Hello extension for TLS, DNS over HTTPS or over QUIC protocols aim to further increase network confidentiality by encrypting the domain names of the visited servers.

In this article, we check whether an attacker able to passively observe the traffic of users could still recover the domain name of websites they visit even if names are encrypted. By relying on large-scale network traces, we show that simplistic features and off-the-shelf machine learning models are sufficient to achieve surprisingly high precision and recall when recovering encrypted domain names. We consider three attack scenarios, i.e., recovering the per-flow name, rebuilding the set of visited websites by a user, and checking which users visit a given target website. We next evaluate the efficacy of padding-based mitigation, finding that all three attacks are still effective, despite resources wasted with padding. We conclude that current proposals for domain encryption may produce a false sense of privacy, and more robust techniques should be envisioned to offer protection to end users.

REFERENCES

  1. [1] Farrell S. and Tschofenig H.. 2014. Pervasive Monitoring Is an Attack. Technical Report 7528. RFC Editor.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. [2] Naylor D., Finamore A., Leontiadis I., Grunenberger Y., Mellia M., Munafò M., Papagiannaki K., and Steenkiste P.. 2014. The cost of the “S” in HTTPS. In Proceedings of the International Conference on Emerging Networking Experiments and Technologies (CoNEXT’14). 133140.Google ScholarGoogle Scholar
  3. [3] Anderson B. and McGrew D.. 2019. TLS beyond the browser: Combining end host and network data to understand application behavior. InProceedings of the Internet Measurement Conference (IMC’19). 379392.Google ScholarGoogle Scholar
  4. [4] Bishop Mike. 2021. Hypertext Transfer Protocol Version 3 (HTTP/3). Internet-Draft draft-ietf-quic-http-34. Internet Engineering Task Force. Retrieved from https://datatracker.ietf.org/doc/html/draft-ietf-quic-http-34.Google ScholarGoogle Scholar
  5. [5] Böttger T., Cuadrado F., Antichi G., Fernandes E., Tyson G., Castro I., and Uhlig S.. 2019. An empirical study of the cost of DNS-over-HTTPS. InProceedings of the Internet Measurement Conference (IMC’19). 1521.Google ScholarGoogle Scholar
  6. [6] Bermudez I., Mellia M., Munafò M., Keralapura R., and Nucci A.. 2012. DNS to the rescue: Discerning content and services in a tangled web. InProceedings of the Internet Measurement Conference (IMC’12). 413426.Google ScholarGoogle Scholar
  7. [7] Vassio L., Giordano D., Trevisan M., Mellia M., and Silva A.. 2017. Users’ fingerprinting techniques from TCP traffic. InProceedings of the ACM CoNEXT Workshop on Big DAta, Machine Learning and Artificial Intelligence for Data Communication Networks (Big-DAMA’17). 4954.Google ScholarGoogle Scholar
  8. [8] Hoffman P. and McManus P.. 2018. DNS Queries over HTTPS (DoH). Technical Report 8484. RFC Editor.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. [9] Huitema Christian, Dickinson Sara, and Mankin Allison. 2022. DNS over Dedicated QUIC Connections. RFC 9250. (May2022). DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. [10] Kosek Mike, Schumann Luca, Marx Robin, Doan Trinh Viet, and Bajpai Vaibhav. 2022. DNS privacy with speed? Evaluating DNS over QUIC and its impact on web performance. In Proceedings of the 22nd ACM Internet Measurement Conference. 4450.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. [11] Rescorla Eric, Oku Kazuho, Sullivan Nick, and Wood Christopher A.. 2021. TLS Encrypted Client Hello. Internet-Draft draft-ietf-tls-esni-13. Internet Engineering Task Force. Retrieved from https://datatracker.ietf.org/doc/html/draft-ietf-tls-esni-13. Work in Progress.Google ScholarGoogle Scholar
  12. [12] Dainotti Alberto, Pescape Antonio, and Claffy Kimberly C.. 2012. Issues and future directions in traffic classification. IEEE Netw. 26, 1 (2012), 3540. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. [13] Mayrhofer Alexander. 2018. Padding Policies for Extension Mechanisms for DNS (EDNS(0)). Technical Report 8467. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. [14] Trevisan Martino, Soro Francesca, Mellia Marco, Drago Idilio, and Morla Ricardo. 2020. Does domain name encryption increase users’ privacy? ACM SIGCOMM Comput. Commun. Rev. 50, 3 (2020), 1622.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. [15] Hu Z., Zhu L., Heidemann J., Mankin A., Wessels D., and Hoffman P.. 2016. Specification for DNS over Transport Layer Security (TLS). Technical Report 7858. RFC Editor.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. [16] Dickinson Sara, Gillmor Daniel Kahn, and Reddy.K Tirumaleswar. 2018. Usage Profiles for DNS over TLS and DNS over DTLS. RFC 8310. (March2018). DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. [17] Schwartz Benjamin M., Bishop Mike, and Nygren Erik. 2021. Service Binding and Parameter Specification via the DNS (DNS SVCB and HTTPS RRs). Internet-Draft draft-ietf-dnsop-svcb-https-07. Internet Engineering Task Force. Retrieved from https://datatracker.ietf.org/doc/html/draft-ietf-dnsop-svcb-https-07.Google ScholarGoogle Scholar
  18. [18] Hintz A.. 2003. Fingerprinting websites using traffic analysis. InProceedings of the Annual Privacy Enhancing Technologies Symposium (PETS’03). 171178.Google ScholarGoogle Scholar
  19. [19] Shi Y. and Biswas S.. 2014. Website fingerprinting using traffic analysis of dynamic webpages. InProceedings of the IEEE Global Communications Conference (GLOBECOM’14). 557563.Google ScholarGoogle Scholar
  20. [20] Gu X., Yang M., and Luo J.. 2015. A novel website fingerprinting attack against multi-tab browsing behavior. InProceedings of the IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD’15). 234239.Google ScholarGoogle Scholar
  21. [21] Arp D., Yamaguchi F., and Rieck K.. 2015. Torben: A practical side-channel attack for deanonymizing Tor communication. InProceedings of the ACM ASIA Conference on Computer and Communications Security (ASIACCS’15). 597602.Google ScholarGoogle Scholar
  22. [22] Gonzalez R., Soriente C., and Laoutaris N.. 2016. User profiling in the time of HTTPS. InProceedings of the Internet Measurement Conference (IMC’16). 373379.Google ScholarGoogle Scholar
  23. [23] Feghhi S. and Leith D.. 2016. A web traffic analysis attack using only timing information. IEEE Trans. Inf. Forens. Secur. 11, 8 (2016), 17471759.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. [24] Lescisin M. and Mahmoud Q.. 2018. Tools for active and passive network side-channel detection for web applications. InProceedings of the IEEE Workshop on Offensive Technologies (WOOT’18).Google ScholarGoogle Scholar
  25. [25] Miller B., Huang L., Joseph A., and Tygar J.. 2014. I know why you went to the clinic: Risks and realization of HTTPS Traffic analysisProceedings of the Annual Privacy Enhancing Technologies Symposium (PETS’14). 143163.Google ScholarGoogle Scholar
  26. [26] Rimmer V., Preuveneers D., Juarez M., Goethem T. Van, and Joosen W.. 2018. Automated website fingerprinting through deep learning. Proceedings of the NDSS).Google ScholarGoogle Scholar
  27. [27] Bhat S., Lu D., Kwon A., and Devadas S.. 2019. Var-CNN: A data-efficient website fingerprinting attack based on deep learning. Proceedings of the Annual Privacy Enhancing Technologies Symposium (PETS’19). 292310.Google ScholarGoogle Scholar
  28. [28] Sirinam P., Imani M., Juarez M., and Wright M.. 2018. Deep fingerprinting: Undermining website fingerprinting defenses with deep learning. Proceedings of the ACM Conference on Computer and Communications Security (CCS’18). 19281943.Google ScholarGoogle Scholar
  29. [29] Wang T., Cai X., Nithyanand R., Johnson R., and Goldberg I.. 2014. Effective attacks and provable defenses for website fingerprinting. Proceedings of the USENIX Security Symposium (USENIX Security’14). 143157.Google ScholarGoogle Scholar
  30. [30] Plonka D. and Barford P.. 2011. Flexible traffic and host profiling via DNS rendezvous. InProceedings of the 1st Securing and Trusting Internet Names Workshop (SATIN’11). 18.Google ScholarGoogle Scholar
  31. [31] Mori T., Inoue T., Shimoda A., Sato K., Harada S., Ishibashi K., and Goto S.. 2016. Statistical estimation of the names of HTTPS servers with domain name graphs. Comput. Commun. 94 (2016), 104113.Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. [32] Lu C., Liu B., Li Z., Hao S., Duan H., Zhang M., Leng C., Liu Y., Zhang Z., and Wu J.. 2019. An end-to-end, large-scale measurement of DNS-over-encryption: How far have we come? In Proceedings of the Internet Measurement Conference (IMC’19). 2235.Google ScholarGoogle Scholar
  33. [33] Hounsel Austin, Borgolte Kevin, Schmitt Paul, Holland Jordan, and Feamster Nick. 2020. Comparing the effects of DNS, dot, and doh on web performance. In Proceedings of the Web Conference 2020. 562572.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. [34] Doan T. V., Tsareva I., and Bajpai V.. 2021. Measuring dns over tls from the edge: adoption, reliability, and response times. In Proceeding of the International Conference on Passive and Active Network Measurement, Springer, 192209.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. [35] Houser R., Li Z., Cotton C., and Wang H.. 2019. An investigation on information leakage of DNS over TLS. InProceedings of the International Conference on Emerging Networking Experiments and Technologies (CoNEXT’19).Google ScholarGoogle Scholar
  36. [36] Siby S., Juarez M., Diaz C., Vallina-Rodriguez N., and Troncoso C.. 2020. Encrypted DNS–> privacy? A traffic analysis perspective. InProceedings of the Network and Distributed System Security Symposium (NDSS’20).Google ScholarGoogle Scholar
  37. [37] Vekshin Dmitrii, Hynek Karel, and Cejka Tomas. 2020. Doh insight: Detecting DNS over https by machine learning. In Proceedings of the 15th International Conference on Availability, Reliability and Security. 18.Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. [38] Hynek Karel and Cejka Tomas. 2020. Privacy illusion: Beware of unpadded DoH. In Proceedings of the 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON’20). IEEE, 06210628.Google ScholarGoogle ScholarCross RefCross Ref
  39. [39] Cheng Jin, He Runkang, Yuepeng E., Wu Yulei, You Junling, and Li Tong. 2020. Real-time encrypted traffic classification via lightweight neural networks. In Proceedings of the IEEE Global Communications Conference (GLOBECOM’20). IEEE, 16.Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. [40] Bushart Jonas and Rossow Christian. 2020. Padding ain’t enough: Assessing the privacy guarantees of encrypted DNS. In Proceedings of the 10th USENIX Workshop on Free and Open Communications on the Internet (FOCI’20).Google ScholarGoogle Scholar
  41. [41] Alec Muffett. Dohot: Making practical use of dns over https over Tor. Retrieved February 15, 2021 from https://github.com/alecmuffett/dohot.Google ScholarGoogle Scholar
  42. [42] Singanamalla Sudheesh, Chunhapanya Suphanat, Vavruša Marek, Verma Tanya, Wu Peter, Fayed Marwan, Heimerl Kurtis, Sullivan Nick, and Wood Christopher. 2021. Oblivious DNS over HTTPS (ODoH): A practical privacy enhancement to DNS. Proceedings on Privacy Enhancing Technologies 4 (2021), 575592.Google ScholarGoogle ScholarCross RefCross Ref
  43. [43] Hoang Nguyen Phong, Niaki Arian Akhavan, Borisov Nikita, Gill Phillipa, and Polychronakis Michalis. 2020. Assessing the privacy benefits of domain name encryption. In Proceedings of the 15th ACM Asia Conference on Computer and Communications Security. 290304.Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. [44] Dyer Kevin P., Coull Scott E., Ristenpart Thomas, and Shrimpton Thomas. 2012. Peek-a-boo, i still see you: Why efficient traffic analysis countermeasures fail. In Proceedings of the IEEE Symposium on Security and Privacy. IEEE, 332346.Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. [45] Shmatikov Vitaly and Wang Ming-Hsiu. 2006. Timing analysis in low-latency mix networks: Attacks and defenses. In European Symposium on Research in Computer Security. Springer, 1833.Google ScholarGoogle Scholar
  46. [46] Trevisan M., Drago I., Mellia M., and Munafo M.. 2016. Towards web service classification using addresses and DNS. InProceedings of the International Wireless Communications and Mobile Computing Conference (IWCMC’16). 3843.Google ScholarGoogle Scholar
  47. [47] Trevisan M., Finamore A., Mellia M., Munafo M., and Rossi D.. 2017. Traffic analysis with off-the-shelf hardware: Challenges and lessons learned. IEEE Commun. Mag. 55, 3 (2017), 163169.Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. [48] Aqeel Waqar, Chandrasekaran Balakrishnan, Feldmann Anja, and Maggs Bruce M.. 2020. On landing and internal web pages: The strange case of Jekyll and Hyde in web performance measurement. In Proceedings of the ACM Internet Measurement Conference. 680695.Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. [49] Hofstede Rick, Čeleda Pavel, Trammell Brian, Drago Idilio, Sadre Ramin, Sperotto Anna, and Pras Aiko. 2014. Flow monitoring explained: From packet capture to data analysis with netflow and ipfix. IEEE Commun. Surv. Tutor. 16, 4 (2014), 20372064.Google ScholarGoogle ScholarCross RefCross Ref
  50. [50] Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., Vanderplas J., Passos A., Cournapeau D., Brucher M., Perrot M., and Duchesnay E.. 2011. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12 (2011), 28252830.Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. [51] Strobl Carolin, Malley James, and Tutz Gerhard. 2009. An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychol. Methods 14, 4 (2009), 323.Google ScholarGoogle ScholarCross RefCross Ref
  52. [52] Jha Nikhil, Trevisan Martino, Vassio Luca, and Mellia Marco. 2022. The internet with privacy policies: Measuring the web upon consent. ACM Trans. Web 16, 3 (2022), 124.Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Attacking DoH and ECH: Does Server Name Encryption Protect Users’ Privacy?

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in

        Full Access

        • Published in

          cover image ACM Transactions on Internet Technology
          ACM Transactions on Internet Technology  Volume 23, Issue 1
          February 2023
          564 pages
          ISSN:1533-5399
          EISSN:1557-6051
          DOI:10.1145/3584863
          • Editor:
          • Ling Liu
          Issue’s Table of Contents

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 23 February 2023
          • Online AM: 9 November 2022
          • Accepted: 27 October 2022
          • Revised: 22 August 2022
          • Received: 3 September 2021
          Published in toit Volume 23, Issue 1

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
        • Article Metrics

          • Downloads (Last 12 months)220
          • Downloads (Last 6 weeks)41

          Other Metrics

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Full Text

        View this article in Full Text.

        View Full Text

        HTML Format

        View this article in HTML Format .

        View HTML Format
        About Cookies On This Site

        We use cookies to ensure that we give you the best experience on our website.

        Learn more

        Got it!