IMR: MT: A Tool for Passively Measuring Internet Censorship

IMR：MT：被动衡量互联网审查的工具

• 批准号: 2323193
• 负责人: David Levin
• 金额: $ 52.73万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2323193&HistoricalAwards=false
• 关键词: IMR; MT; Tool; Passively; Measuring

Billions of people worldwide live in regions that perform automated censorship of Internet traffic and other forms of traffic tampering. Understanding how users are affected by such censorship is of critical importance in developing technologies and policies that promote more free and open access to information. Prior efforts to measure Internet censorship have focused on active measurements, which involve procuring vantage points and then issuing queries from a test-list of domain names or keywords to determine what is being censored where. While these efforts have proven invaluable, relying on active measurements alone imposes several limitations, chief among them being that they can only measure what could be censored, not what users are actively experiencing. This project's novelties are a collection of measurement tools that passively detect censorship as it occurs with real users' Internet traffic. The project's broader significance and importance are that it allows datasets to be collected that are not possible with active measurements alone; the proposed project's passive techniques can rapidly detect newly censored content, and they capture what users are actively experiencing.The central insight behind the project is that censorship can be detected through "tamper signatures": tell-tale signs of traffic tampering by a third party, such as suspiciously timed connection tear-down packets or duplicate responses to unencrypted queries. The project is organized along different deployment scenarios that each raise unique technical challenges when detecting tamper signatures: a customized measurement tool for server-side deployments, one for client-side deployments, and one for in-network deployments (such as at an Internet Service Provider). This collection of measurement tools is expected to permit censorship measurements at many disparate vantage points around the world. The project is thus also developing techniques that will allow multiple networks to share their data safely and responsibly.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

全球有数十亿人生活在对互联网流量和其他形式的流量篡改进行自动审查的地区。 了解用户如何受到此类审查的影响对于开发促进更自由和开放地获取信息的技术和政策至关重要。 此前衡量互联网审查制度的努力主要集中在主动衡量上，其中包括获取有利位置，然后从域名或关键字测试列表中发出查询，以确定哪些内容在何处受到审查。 虽然这些努力已被证明是无价的，但仅依靠主动测量会带来一些限制，其中最主要的是它们只能测量可能被审查的内容，而不能测量用户正在主动体验的内容。 该项目的新颖之处在于一系列测量工具，可以被动检测真实用户互联网流量中发生的审查情况。 该项目更广泛的意义和重要性在于，它可以收集仅通过主动测量无法收集到的数据集；拟议项目的被动技术可以快速检测新审查的内容，并捕获用户正在主动体验的内容。该项目背后的核心见解是可以通过“篡改签名”来检测审查：第三方流量篡改的迹象，例如可疑的定时连接拆除数据包或对未加密查询的重复响应。 该项目按照不同的部署场景进行组织，每种场景在检测篡改签名时都会带来独特的技术挑战：一种用于服务器端部署的定制测量工具，一种用于客户端部署，一种用于网络内部署（例如在互联网服务中）提供者）。 这一系列测量工具预计将允许在世界各地许多不同的有利位置进行审查测量。 因此，该项目还在开发允许多个网络安全、负责任地共享数据的技术。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。