Abstract: The arms race between Internet freedom advocates and censors has catalyzed the emergence of sophisticated blocking techniques and directed significant research emphasis toward the development of automated censorship measurement and evasion tools based on packet manipulation. However, we observe that the probing process of censorship middleboxes using state-of-the-art evasion tools can be easily fingerprinted by censors, necessitating detection-resilient probing techniques.
We validate our hypothesis by developing a real-time detection approach that utilizes Machine Learning (ML) to detect flow-level packet-manipulation and an algorithm for IP-level detection based on Threshold Random Walk (TRW).
Abstract: Since 2006, Turkmenistan has been listed as one of the few Internet enemies by Reporters without Borders due to its extensively censored Internet and strictly regulated information control policies. Existing reports of filtering in Turkmenistan rely on a small number of vantage points or test a small number of websites. Yet, the country’s poor Internet adoption rates and small population can make more comprehensive measurement challenging. With a population of only six million people and an Internet penetration rate of only 38%, it is challenging to either recruit in-country volunteers or obtain vantage points to conduct remote network measurements at scale.