GFWeb: Measuring the Great Firewall's Web Censorship at Scale


Nguyen Phong Hoang, University of British Columbia and University of Chicago; Jakub Dalek and Masashi Crete-Nishihata, Citizen Lab - University of Toronto; Nicolas Christin, Carnegie Mellon University; Vinod Yegneswaran, SRI International; Michalis Polychronakis, Stony Brook University; Nick Feamster, University of Chicago


Censorship systems such as the Great Firewall (GFW) have been continuously refined to enhance their filtering capabilities. However, most prior studies, and in particular the GFW, have been limited in scope and conducted over short time periods, leading to gaps in our understanding of the GFW's evolving Web censorship mechanisms over time. We introduce GFWeb, a novel system designed to discover domain blocklists used by the GFW for censoring Web access. GFWeb exploits GFW's bidirectional and loss-tolerant blocking behavior to enable testing hundreds of millions of domains on a monthly basis, thereby facilitating large-scale longitudinal measurement of HTTP and HTTPS blocking mechanisms.

Over the course of 20 months, GFWeb has tested a total of 1.02 billion domains, and detected 943K and 55K pay-level domains censored by the GFW's HTTP and HTTPS filters, respectively. To the best of our knowledge, our study represents the most extensive set of domains censored by the GFW ever discovered to date, many of which have never been detected by prior systems. Analyzing the longitudinal dataset collected by GFWeb, we observe that the GFW has been upgraded to mitigate several issues previously identified by the research community, including overblocking and failure in reassembling fragmented packets. More importantly, we discover that the GFW's bidirectional blocking is not symmetric as previously thought, i.e., it can only be triggered by certain domains when probed from inside the country. We discuss the implications of our work on existing censorship measurement and circumvention efforts. We hope insights gained from our study can help inform future research, especially in monitoring censorship and developing new evasion tools.

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