Many techniques have been introduced to measure network interference-tampering performed by nation-state censors or corporate firewalls to block unwanted traffic. How-ever, virtually all prior measurement techniques require some degree of participation from endpoints within each country of study: including VPNs, cloud providers, or volunteers willing to run measurement software on their personal devices at their own risk. However, such endpoints are not always available in all countries that tamper with connections, leaving many networks unmeasurable. In this paper, we present the first global, active, network interference measurements that require no participating end-points within any country of study. Our techniques extend two recent studies that use packet sequences that trigger network interference from outside the country of study by tricking middleboxes into believing a connection exists. Our system, Mint, generalizes and automates this approach-which had previously only been applied to two countries-to allow it to apply to the global IPv4 and IPv6 Internet. We use Mint to conduct the first global measurements of network interference without using any participating endpoints, and the first comprehensive scans of IPv6 interference. We show that we are able to measure networks, autonomous systems, and even entire countries that previous methods could not. We also present several case studies that highlight how our tool can be used to perform new measurement studies of network interference.

In-network devices around the world monitor and tamper with connections for many reasons, including intrusion prevention, combating spam or phishing, and country-level censorship. Connection tampering seeks to block access to specific domain names or keywords, and it affects billions of users worldwide with little-to-no transparency. To detect, diagnose, and measure connection-level blocking, "active" measurement techniques originate queries with domains or keywords believed to be blocked and send them from vantage points within networks of interest. Active measurement efforts have been critical to understanding how traffic tampering occurs, but they inherently are unable to capture critical parts of the picture. For instance, knowing the set of domains in a block-list (i.e., what could get blocked) is not the same as knowing what real users are actively experiencing (i.e., what is actively getting blocked).

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 handful 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.

Decades of work on censorship evasion have resulted in myriad ways to empower clients with the ability to access censored content, but to our knowledge all of them have required some degree of client-side participation. Having to download and run anti-censorship software can put users at risk, and does not help the many users who do not even realize they are being censored in the first place.

Researchers and censoring regimes have long engaged in a cat-and-mouse game, leading to increasingly sophisticated Internet-scale censorship techniques and methods to evade them. In this paper, we take a drastic departure from the previously manual evade-detect cycle by developing techniques to automate the discovery of censorship evasion strategies. We present Geneva, a novel genetic algorithm that evolves packet-manipulation-based censorship evasion strategies against nation-state level censors. Geneva composes, mutates, and evolves sophisticated strategies out of four basic packet manipulation primitives (drop, tamper headers, duplicate, and fragment). With experiments performed both in-lab and against several real censors (in China, India, and Kazakhstan), we demonstrate that Geneva is able to quickly and independently re-derive most strategies from prior work, and derive novel subspecies and altogether new species of packet manipulation strategies. Moreover, Geneva discovers successful strategies that prior work posited were not effective, and evolves extinct strategies into newly working variants. We analyze the novel strategies Geneva creates to infer previously unknown behavior in censors. Geneva is a first step towards automating censorship evasion; to this end, we have made our code and data publicly available.