Learning Nation-State Censorship with Genetic Algorithms


Censorship evasion is a cat-and-mouse game between nation-states and researchers. Today’s evade/detect cycle is largely manual: researchers first actively measure censors' networks to learn about how they operate, and then develop strategies to exploit shortcomings in the censors' designs and implementations. This unfortunately gives censors an asymmetric advantage: the time to manually measure and learn about censors often exceeds the time it takes a censor to patch its network. In this talk, we propose a drastic departure from the manual evade/detect cycle: using artificial intelligence to adaptively probe censoring regimes and automatically discover strategies for circumventing them. We will present the design of our genetic algorithm-based architecture, and the results thus far from applying it to in-lab and real network censors, including the Great Firewall of China. Our architecture was able to independently derive virtually all prior work on client-side evasion of on-path censors, and has discovered wholly new strategies, giving us novel insight into the functionality of nation-state censors. We will also present ongoing and future applications of this approach.

Apr 16, 2019 1:30 PM
AIMS 2019: San Diego