FilteredWeb: A framework for the automated search-based discovery of blocked URLs - Darer, A; Farnan, O; Wright, J (IEEE/IFIP, 2017)
Various methods have been proposed for creating and maintaining lists of potentially filtered URLs to allow for measurement of ongoing internet censorship around the world. Whilst testing a known resource for evidence of filtering can be relatively simple, given appropriate vantage points, discovering previously unknown filtered web resources remains an open challenge. We present a new framework for automating the process of discovering filtered resources through the use of adaptive queries to well-known search engines. Our system applies information retrieval algorithms to isolate characteristic linguistic patterns in known filtered web pages; these are then used as the basis for web search queries. The results of these queries are then checked for evidence of filtering, and newly discovered filtered resources are fed back into the system to detect further filtered content. Our implementation of this framework, applied to China as a case study, shows that this approach is demonstrably effective at detecting significant numbers of previously unknown filtered web pages, making a significant contribution to the ongoing detection of internet filtering as it develops. Our tool is currently deployed and has been used to discover 1355 domains that are poisoned within China as of Feb 2017—30 times more than are contained in the most widely-used public filter list. Of these, 759 are outside of the Alexa Top 1000 domains list, demonstrating the capability of this framework to find more obscure filtered content. Further, our initial analysis of filtered URLs, and the search terms that were used to discover them, gives further insight into the nature of the content currently being blocked in China.