Automatically learning how to evade nation-state censors

Research on automating censorship evasion

Researchers and censoring regimes have long engaged in a cat-and-mouse game, leading to increasingly sophisticated Internet-scale censorship techniques and methods to evade them. In this work, we take a drastic departure from the previously manual evade/detect cycle by developing techniques to automate the discovery of censorship evasion strategies.

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Geneva: Evolving censorship evasion

We have developed Geneva (Genetic Evasion), a novel genetic algorithm that evolves packet-manipulation-based censorship evasion strategies against nation-state level censors. Geneva re-derived virtually all previously published evasion strategies within hours, and has discovered new ways of circumventing censorship in China, India, and Kazakhstan.

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Latest News


Geneva source code for executing Geneva strategies is now public!


Kevin presented Geneva at ACM CCS 2019.


Kevin and Dave gave a talk, "Combating Censorship with Artificial Intelligence," at Science on Tap.


A paper introducing Geneva was accepted to ACM CCS 2019! Congrats to students Kevin Bock and George Hughey!


Dave gave a talk, "Automatically Learning How to Evade Censorship," at USENIX ScAINet 2019.


Kevin and George gave a talk, "Learning Nation-State Censorship with Genetic Algorithms," at AIMS 2019.


This project is done by students in Breakerspace, a lab at the University of Maryland dedicated to scaling-up undergraduate research in computer and network security.


This work is supported by the Open Technology Fund and the National Science Foundation.