CenRL: A Framework for Performing Intelligent Censorship Measurements

Active Internet measurements are crucial for exposing the increasing frequency and severity of global Internet censorship. However, current measurement efforts are constrained by limited resources and time, and reacting to new censorship events remains a largely manual process reliant on rapid signals. As a result, creating a comprehensive and realtime picture of Internet censorship remains a key challenge. In this work, we introduce CenRL, an intelligent censorship measurement framework that leverages reinforcement learning to optimize and automate censorship measurements. We model the censorship measurement process as a multi-armed bandit problem and design CenRL agents to address two key tasks: maximizing the detection of blocked websites within a network and automatically responding to blocking changes in dynamic censorship environments. We demonstrate CenRL's effectiveness through realistic simulated experiments in three highly censored regions (China, Russia, and Kazakhstan) and realworld censorship measurements across vantage points in 15 countries with diverse censorship policies. Our controlled experiments demonstrate that CenRL significantly outperforms the state of the art measurement processes, finding 75% of blocked websites in less than half the number of measurements while identifying censorship changes up to seven times faster. Our real-world experiments confirm this advantage across multiple censorship environments, showing that CenRL can find 2.5 times more blocked websites on average compared to existing measurement strategies. Our study demonstrates the potential of using reinforcement learning to provide deeper insights into restrictions on online freedom.