Geneva: Evolving Censorship Evasion Strategies

Researchers and censoring regimes have long engaged in a cat-andmouse game, leading to increasingly sophisticated Internet-scale censorship techniques and methods to evade them. In this paper, we take a drastic departure from the previously manual evadedetect cycle by developing techniques to automate the discovery of censorship evasion strategies. We present Geneva, a novel genetic algorithm that evolves packet-manipulation-based censorship evasion strategies against nation-state level censors. Geneva composes, mutates, and evolves sophisticated strategies out of four basic packet manipulation primitives (drop, tamper headers, duplicate, and fragment). With experiments performed both in-lab and against several real censors (in China, India, and Kazakhstan), we demonstrate that Geneva is able to quickly and independently re-derive most strategies from prior work, and derive novel subspecies and altogether new species of packet manipulation strategies. Moreover, Geneva discovers successful strategies that prior work posited were not effective, and evolves extinct strategies into newly working variants. We analyze the novel strategies Geneva creates to infer previously unknown behavior in censors. Geneva is a first step towards automating censorship evasion; to this end, we have made our code and data publicly available. CCS CONCEPTS • Social and professional topics → Technology and censorship; • Computing methodologies → Genetic algorithms; • General and reference → Measurement.