|
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. |
|
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. |
| 11/14/2019 |
Geneva source code for executing Geneva strategies is now public! |
| 11/14/2019 |
Kevin presented Geneva at ACM CCS 2019. |
| 09/30/2019 |
Kevin and Dave gave a talk, "Combating Censorship with Artificial Intelligence," at Science on Tap. |
| 09/23/2019 |
A paper introducing Geneva was accepted to ACM CCS 2019! Congrats to students Kevin Bock and George Hughey! |
| 08/12/2019 |
Dave gave a talk, "Automatically Learning How to Evade Censorship," at USENIX ScAINet 2019. |
| 04/17/2019 |
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.