ISN2: Interpretable and Automated Detection of Illicit Online Commercial Enterprises

该奖项将通过研究自动识别主要通过在线广告运营的非法商业企业的方法，加强国家安全、繁荣和健康。虽然许多合法企业使用在线平台，如广告服务、招聘广告和审查委员会，但非法企业也利用这些服务，并且可能难以区分它们。利用这些平台的非法业务往往与人口贩运活动有关。该项目开发了分析来自多个来源的大量在线数据的方法，以创建可解释的风险评分，从而有助于发现非法业务。该项目与致力于打击人口贩运的数据分析非营利组织全球解放网络（Global Emancipation Network）合作，将特定业务的数据与可公开获得的许可文件和法庭记录的数据融合在一起，以更好地发现可疑活动并指导资源有限的拦截工作。其结果将使反贩运工作现代化，以跟上贩运者使用的复杂战略。该奖项将为教育研究生提供支持，以满足非法支持网络研究的新需求，从而为政策提供信息。这项研究使用了一个庞大的现有数据库，其中收集了来自深度和开放网络的数据，将为自动检测非法业务建立风险评分。风险评分是一种线性分类模型，只需要用户对一些小数字进行加减乘就可以进行预测，因此这些模型易于应用和理解。来自非法企业的广告信息具有明显的特征，例如数据混淆、非随机拼写错误、高频率出现词汇表外和不寻常的单词，以及频繁使用Unicode字符，使自然语言处理变得困难。将风险评分学习问题表述为一个非线性混合整数优化问题。分析框架利用并扩展了最先进的优化和统计学习技术，并将产生可扩展的分支切断程序来解决大型训练集的学习问题。它将采用半监督学习方法，使用标记和未标记的数据来生成更好的风险评分。风险评分的绩效评估将以合法和非法按摩业务的真实数据为依据。研究结果将推广到不同的数据平台，在这项工作中开发的方法有望转化为其他部门的人口贩运侦查。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award will enhance national security, prosperity and health by studying ways to automatically identify illicit commercial enterprises that operate primarily via online advertising. While many legitimate enterprises use online platforms, such as advertisement services, job recruitment ads, and review boards, illicit business also make use of these services, and it may be difficult to distinguish between them. Illicit business using these platforms are often associated with human trafficking activity. This project develops methods to analyze large amounts of online data from multiple sources to create an interpretable risk score that facilitates detection of illicit business. In partnership with the Global Emancipation Network, a data analytics non-profit dedicated to countering human trafficking, the project will fuse data from business-specific operations with data from publicly available licensing documents and court records to better detect suspicious activity and guide resource-constrained interdiction efforts. The results will modernize anti-trafficking efforts to keep pace with the complex strategies used by traffickers. The award will provide support to educate graduate students to meet the emerging needs of illicit support network research to inform policy.Using a large existing database of scraped data from the deep and open web, this research will build risk scores for automatically detecting illicit businesses. Risk scores are linear classification models that only require users to add, subtract and multiply a few small numbers in order to make a prediction, as such, these models are easy to apply and understand. Information in ads from illicit businesses has distinguishing features, such as data obfuscation, non-random misspellings, high occurrences of out-of-vocabulary and unusual words, and frequent use of Unicode characters, making natural language processing difficult. The risk score learning problem is formulated as a nonlinear mixed-integer optimization problem. The analytical framework leverages and extends state-of-the-art techniques from optimization and statistical learning and will produce a scalable branch-and-cut procedure to solve the learning problem over large training sets. It will employ semi-supervised learning methods to use both labeled and unlabeled data to generate better risk scores. The performance evaluation of the risk scores will be informed by real data from legitimate and illicit massage businesses. The research results will be generalizable to different data platforms, and the methods developed in this work is expected to be translatable to detection of human trafficking in other sectors.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.