Senser is a distributed censorship detection and circumvention system for the web. Senser operates as a network of proxies located at different vantage points on the Internet (some of which may be subject to censorship). Clients query a random subset of Senser proxies for compact descriptions of a desired web page, and apply consensus and matching algorithms to the returned results to locally render a “majority” web page. To ensure diverse selections of proxies (and consequently decrease an adversary’s ability to censor a majority of them), Senser leverages Internet mapping systems that accurately predict AS-level paths between available proxies and the desired web page. We demonstrate using a deployment of Senser on Amazon EC2 that Senser detects and mitigates censorship attempts — even by large collections of autonomous systems — while incurring reasonable performance overheads.
Jordan Wilberding, Andrew Yates, Micah Sherr, and Wenchao Zhou. Validating Web Content with Senser. In Annual Computer Security Applications Conference (ACSAC), December 2013. [.pdf]
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