Automatically Learning How to Evade Censorship

Monday, August 12, 2019 - 12:00 pm12:30 pm

Dave Levin, University of Maryland

Abstract: 

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. This talk will introduce a drastic departure from the previously manual evade-detect cycle: applying artificial intelligence techniques to automate the discovery of censorship evasion strategies. We will demonstrate that, by training AI against live censors, one can glean new insights into how censorship works around the world, and how to circumvent it. After a brief demonstration of a proof of concept involving genetic algorithms, the bulk of the talk will focus on future directions and open questions, including: Does automating the evade/detect cycle ultimately benefit the censor? What protocols can be automatically learned? And, can training be collected from many users and vantage points?

Dave Levin, University of Maryland

Dave Levin is an Assistant Professor of Computer Science at the University of Maryland. His research centers on network security, measurement, and building secure systems. He has received multiple best paper awards, the IRTF Applied Networking Research Prize, the IEEE Cybersecurity Award for Innovation, and a Microsoft Live Labs Fellowship. He is also Co-Chair of UMD’s CS Honors program, and the founder of Breakerspace, a research lab for undergraduate students.

BibTeX
@conference {238507,
author = {Dave Levin},
title = {Automatically Learning How to Evade Censorship},
year = {2019},
address = {Santa Clara, CA},
publisher = {{USENIX} Association},
month = aug,
}