The Internet has become a hostile place for users’ traffic. Network-based actors, including ISPs and governments, increasingly practice sophisticated forms of censorship, content injection, and traffic throttling, as well as surveillance and other privacy violations. My work attempts to expose these threats and develop technologies to better safeguard users. Detecting and defending against adversarial networks is challenging, especially at global scale, due to the Internet’s vast size and heterogeneity, the powerful capabilities of in-network threat actors, and the lack of ground-truth on the counterfactual traffic that would exist in the absence of interference. Overcoming these challenges requires new techniques and systems, both for collecting and interpreting evidence of hostile networks and for building defensive tools that effectively meet user needs.

In this talk, I’ll first cover my approach to monitoring Internet censorship. I introduced an entirely new family of censorship measurement techniques, based on network side-channels, that can remotely detect censorship events occurring between distant pairs of network locations. To overcome the systems and data science challenges of operating these techniques and synthesizing their results into a holistic view of online censorship, my students and I created Censored Planet, a censorship observatory that continuously tests the reachability of thousands of popular or sensitive sites from over 100,000 vantage points in 221 countries. Next, I’ll discuss our efforts to understand and defend the consumer VPN ecosystem. Although millions of end-users rely on VPNs to protect their privacy and security, this multibillion-dollar industry includes numerous snakeoil products, is laxly regulated, and remains severely understudied. To address this, my lab created VPNalyzer, a project that aims to bring transparency and better security to consumer VPNs. Our work includes a cross-platform test suite that crowd-sources VPN security testing, coupled with large-scale user studies that aim to understand the needs and threat models of VPN users.

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Mark Huasong Meng (National University of Singapore), Qing Zhang (ByteDance), Guangshuai Xia (ByteDance), Yuwei Zheng (ByteDance), Yanjun Zhang (The University of Queensland), Guangdong Bai (The University of Queensland), Zhi Liu (ByteDance), Sin G. Teo (Agency for Science, Technology and Research), Jin Song Dong (National University of Singapore)

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Xiao Yi (The Chinese University of Hong Kong), Yuzhou Fang (The Chinese University of Hong Kong), Daoyuan Wu (The Chinese University of Hong Kong), Lingxiao Jiang (Singapore Management University)

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Rei Yamagishi, Shinya Sasa, and Shota Fujii (Hitachi, Ltd.)

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