🤖 AI Summary
This study systematically analyzes the nationwide internet shutdown in Iran in January 2026, triggered by protests, by integrating heterogeneous data sources—including network measurements, gray literature, and private observations—for the first time. The research reconstructs the temporal dynamics of the blackout, identifies censorship signatures, and evaluates the real-world effectiveness of circumvention tools under intense state control. By synthesizing network measurement data, cross-validation techniques, and time-series modeling, the work unifies fragmented observations to reveal a “new normal” in communications before and after the shutdown. It quantifies the efficacy of mainstream circumvention methods and comprehensively characterizes the scale, complexity, and evolving dynamics of state-citizen interactions during a national-scale internet blackout.
📝 Abstract
This paper analyzes the Internet shutdown that occurred in Iran in January 2026 in the context of protests, focusing on its impact on the country's digital communication infrastructure and on information access and control dynamics. The scale, complexity, and nation-state nature of the event motivate a comprehensive investigation that goes beyond isolated reports, aiming to provide a unified and systematic understanding of what happened and how it was observed. The study is guided by a set of research questions addressing: the characterization of the shutdown via the timeline of the disruption events and post-event "new normal"; the detectability of the event, encompassing monitoring initiatives, measurement techniques, and precursory signals; and the interplay between censorship and circumvention, assessing both the imposed restrictions and the effectiveness of tools designed to bypass them. To answer these questions, we adopt a multi-source, multi-perspective methodology that integrates heterogeneous public data, primarily from grey literature produced by network measurement and monitoring initiatives, complemented by additional private measurements. This approach enables a holistic view of the event and allows us to reconcile and compare partial observations from different sources.