ISN2: Interpretable and Automated Detection of Illicit Online Commercial Enterprises

该奖项将通过研究如何自动识别主要通过在线广告经营的非法商业企业来加强国家安全、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，可能很难区分它们。利用这些平台的非法交易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分数，以便于发现非法交易。该项目将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合作，将特定业务行动的数据与公开可用的许可证文件和法庭记录的数据融合，以更好地发现可疑活动并指导资源受限的拦截工作。这一结果将使打击人口贩运的努力现代化，以跟上人贩子使用的复杂战略。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，为政策提供信息。这项研究将使用从深度和开放的网络中收集的大型现有数据库，建立自动检测非法商业的风险评分。风险分数是线性分类模型，用户只需加、减、乘几个小数字就可以做出预测，因此，这些模型很容易应用和理解。非法商业广告中的信息具有明显的特点，如数据混淆、非随机拼写错误、词汇表外和不常见单词的高发率以及频繁使用Unicode字符，这使得自然语言处理变得困难。风险分值学习问题被描述为一个非线性混合整数优化问题。该分析框架利用并扩展了优化和统计学习的最新技术，并将产生一个可扩展的分支-切割过程来解决大型训练集上的学习问题。它将使用半监督学习方法，使用标记和未标记的数据来生成更好的风险分数。风险得分的绩效评估将由合法和非法按摩企业的真实数据提供信息。研究结果将可推广到不同的数据平台，这项工作中开发的方法预计将可用于其他部门的人口贩运检测。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.