TWC: Small: Understanding and Defending Against Crowdsourced Online Identities

TWC：小：理解和防御众包在线身份

• 批准号: 1224100
• 负责人: Ben Zhao
• 金额: $ 49.47万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1224100&HistoricalAwards=false
• 关键词: TWC; Small; Understanding; Defending; Against

Remarkable things can be achieved by harnessing power of the masses. By breaking down tasks and distributing to users, "crowdsourcing" systems can accomplish complex tasks such as translating books or creating 3D photo tours. Unfortunately, the opposite also holds: misuse of these systems creates powerful tools that can compromise the security of online communities, since today's security mechanisms focus on defending against automated scripts and fake accounts, but not real users.In a malicious crowdsourcing system, customers initiate campaigns, and workers are paid for tasks such as creating Facebook accounts, posting fake Yelp reviews, or starting rumors on Twitter, with results indistinguishable from those of normal users. This type of malicious activity is called crowdturfing, because of its similarity to both crowd-sourcing systems and "astroturfing." The PIs have already found example sites today, and early measurements show some that generate over $1Million in annual revenue, while growing exponentially in users and revenue.This project studies crowdturfing systems in detail via measurements and experiments, and use those results to develop robust defenses against them. Measurements and interviews will be used to study their support structure and incentives; develop endhost-based techniques to mark their results and economic solutions that reduce incentives for customers and workers; and build effective detection systems that identify crowdturfing in different domains using per-user behavioral models.This work can change the way we view security in online communities, and its results can guide the deployment of new protection mechanisms that target crowdsourced fake user accounts and activities.

发挥群众的力量，可以成就伟大的事业。通过分解任务并分发给用户，“众包”系统可以完成复杂的任务，例如翻译书籍或创建 3D 照片游览。 不幸的是，相反的情况也成立：滥用这些系统会产生强大的工具，可能危及在线社区的安全，因为当今的安全机制侧重于防御自动脚本和虚假帐户，而不是真正的用户。在恶意众包系统中，客户发起攻击活动中，工作人员的任务是获得报酬，例如创建 Facebook 帐户、发布虚假 Yelp 评论或在 Twitter 上散布谣言，其结果与普通用户没有什么区别。这种类型的恶意活动被称为“众包草皮”，因为它与众包系统和“astroturfing”相似。今天，PI 已经找到了示例网站，早期测量显示，一些网站的年收入超过 100 万美元，同时用户和收入呈指数级增长。该项目通过测量和实验详细研究了众草皮系统，并利用这些结果来开发稳健的系统对他们的防御。将通过测量和访谈来研究他们的支持结构和激励措施；开发基于终端主机的技术来标记其结果和经济解决方案，以减少对客户和工人的激励；并建立有效的检测系统，使用每个用户的行为模型识别不同领域的众包行为。这项工作可以改变我们看待在线社区安全的方式，其结果可以指导针对众包虚假用户帐户和活动的新保护机制的部署。