TWC: Small: Collaborative: Cracking Down Online Deception Ecosystems

TWC：小型：协作：打击在线欺骗生态系统

• 批准号: 1526254
• 负责人: Duen Horng Chau
• 金额: $ 24.98万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1526254&HistoricalAwards=false
• 关键词: TWC; Small; Collaborative; Cracking; Down

Used by hundreds of millions of people every day, online services are central to everyday life. Their popularity and impact make them targets of public opinion skewing attacks, in which those with malicious intent manipulate the image of businesses, mobile applications and products. Website owners often turn to crowdsourcing sites to hire an army of professional fraudsters to paint a fake flattering image for mediocre subjects or trick people into downloading malicious software. This research aims to disrupt fraudulent job markets, identify behavioral differences between fraudsters and honest users, and design fraud detection methods for popular crowdsourcing sites such as Yelp and Google Play. Detecting fraudulent information and malicious behavior serves to improve the quality of life of online service users, help reduce the distribution and impact of malware, and protect the credibility of online services.The overarching goal of this project is to significantly increase the complexity and cost of attacks that seek to undetectably influence the ranking and public image of online service subjects. The project consists of two thrusts: modeling and detecting search rank fraud. The modeling thrust dissects fraudulent job markets supported by crowdsourcing sites, documents and models their employer-to-worker interactions and equilibria, and identifies the subjects they target. The researchers will monitor subjects and leverage community expertise and malware detection tools to create large-scale, ground truth datasets in order to assist the effort to identify temporal, spatial and social indicators as well as patterns that differentiate fraudsters from legitimate users. The detection thrust builds upon game theory to develop solutions that disrupt detected, active fraudulent jobs. The researchers will also design scalable search rank fraud detection techniques and algorithms - built on the indicators and patterns identified in the modeling thrust to detect fraudulent, search rank altering behaviors - for online services. The researchers will devise graph-based methods that identify suspicious connections among users and flag multiple colluding accounts. This final aspect uncovers not only targets of large-scale review campaigns but also less popular subjects, whose rank is impacted by even a few fraudulent reviews.

每天有数亿人使用在线服务，在线服务是日常生活的中心。它们的受欢迎程度和影响使它们成为公众舆论歪曲攻击的目标，其中那些恶意操纵企业、移动的应用程序和产品的形象。网站所有者经常求助于众包网站，雇佣一群专业的欺诈者为平庸的对象描绘一个虚假的奉承形象，或者欺骗人们下载恶意软件。这项研究旨在扰乱欺诈性就业市场，识别欺诈者和诚实用户之间的行为差异，并为Yelp和Google Play等流行的众包网站设计欺诈检测方法。检测欺诈信息和恶意行为有助于提高在线服务用户的生活质量，帮助减少恶意软件的传播和影响，并保护在线服务的可信度。该项目的总体目标是显著增加攻击的复杂性和成本，这些攻击试图以不可察觉的方式影响在线服务主体的排名和公众形象。该项目包括两个重点：建模和检测搜索排名欺诈。建模推力剖析了由众包网站支持的欺诈性就业市场，记录和建模了他们的雇主与工人的互动和平衡，并确定了他们的目标对象。研究人员将监测受试者，并利用社区专业知识和恶意软件检测工具创建大规模的地面实况数据集，以帮助识别时间，空间和社会指标以及区分欺诈者与合法用户的模式。检测推力建立在博弈论的基础上，以开发破坏检测到的、活跃的欺诈性工作的解决方案。研究人员还将设计可扩展的搜索排名欺诈检测技术和算法-建立在建模推力中识别的指标和模式上，以检测欺诈性，搜索排名改变行为-用于在线服务。研究人员将设计基于图形的方法，以识别用户之间的可疑联系，并标记多个串通账户。这最后一个方面不仅揭示了大规模评论活动的目标，而且还揭示了不太受欢迎的主题，即使是一些欺诈性评论也会影响这些主题的排名。