How Great is the Great Firewall? Measuring China's DNS Censorship
Not on the map. Why?
Abstract (arXiv)
The DNS filtering apparatus of China's Great Firewall (GFW) has evolved considerably over the past two decades. However, most prior studies of China's DNS filtering were performed over short time periods, leading to unnoticed changes in the GFW's behavior. In this study, we introduce GFWatch, a large-scale, longitudinal measurement platform capable of testing hundreds of millions of domains daily, enabling continuous monitoring of the GFW's DNS filtering behavior. We present the results of running GFWatch over a nine-month period, during which we tested an average of 411M domains per day and detected a total of 311K domains censored by GFW's DNS filter. To the best of our knowledge, this is the largest number of domains tested and censored domains discovered in the literature. We further reverse engineer regular expressions used by the GFW and find 41K innocuous domains that match these filters, resulting in overblocking of their content. We also observe bogus IPv6 and globally routable IPv4 addresses injected by the GFW, including addresses owned by US companies, such as Facebook, Dropbox, and Twitter. Using data from GFWatch, we studied the impact of GFW blocking on the global DNS system. We found 77K censored domains with DNS resource records polluted in popular public DNS resolvers, such as Google and Cloudflare. Finally, we propose strategies to detect poisoned responses that can (1) sanitize poisoned DNS records from the cache of public DNS resolvers, and (2) assist in the development of circumvention tools to bypass the GFW's DNS censorship.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jun 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 2; 2024: 1; 2025: 1.
4 papers.
- 2025R1dacted: Investigating Local Censorship in DeepSeek's R1 Language Modelcited by 7
- 2023Amoeba: Circumventing ML-supported Network Censorship via Adversarial Reinforcement Learningcited by 2
- 2023Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learningcited by 1
- 2024Automatic Generation of Web Censorship Probe Listscited by 0
- 2025R1dacted: Investigating Local Censorship in DeepSeek's R1 Language Model19 May 2025
- 2024Automatic Generation of Web Censorship Probe Lists11 Jul 2024
- 2023Amoeba: Circumventing ML-supported Network Censorship via Adversarial Reinforcement Learning31 Oct 2023
- 2023Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learning3 Feb 2023