Abstract: DNS manipulation is one of the most prevalent and effective techniques for censoring Internet access and interfering with users' online activities worldwide. Reliable detection of DNS tampering is crucial, but challenging due to evolving censorship tactics and the lack of complete ground truth data. In this paper, we demonstrate the power of machine learning (ML) in addressing these challenges by applying supervised and unsupervised models to recent global DNS measurement data collected by the Open Observatory of Network Interference (OONI).
Abstract: Internet censorship research has been centered around notorious censors (e.g., China, Russia, and Iran) since they have developed complex and intricate censorship infrastructures that affect huge populations of people. However, there are other state-sponsored censors that deploy filtering apparatus as extensively, but which come from countries with a small population. These censors are often overlooked by the censorship research community due to the difficult nature in studying them. Current methods that exist to study censorship in such countries include deploying physical probes, recruiting volunteers to run experiments, using VPNs, or taking advantage of public infrastructures.
The Great Firewall of China (GFW) has long used DNS packet injection to censor Internet access. In this work, we analyze the DNS injection behavior of the GFW over a period of nine months using the Alexa top 1M domains as a test list. We first focus …
The prevalence of Internet censorship has prompted the creation of several measurement platforms for monitoring filtering activities. An important challenge faced by these platforms revolves around the trade-off between depth of measurement and …
Tor and I2P are well-known anonymity networks used by many individuals to protect their online privacy and anonymity. Tor's centralized directory services facilitate the understanding of the Tor network, as well as the measurement and visualization …