ISN2: Interpretable and Automated Detection of Illicit Online Commercial Enterprises

ISN2：非法在线商业企业的可解释和自动检测

• 批准号: 1936331
• 负责人: Osman Ozaltin
• 金额: $ 45.72万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1936331&HistoricalAwards=false
• 关键词: ISN2; Interpretable; Automated; Detection; Illicit

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.

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来增强国家安全、繁荣和健康。 虽然许多合法企业使用在线平台，例如广告服务、招聘广告和评论板，但非法企业也使用这些服务，并且可能很难区分它们。 使用这些平台的非法业务通常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，从而有助于检测非法业务。 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合作，将特定业务运营的数据与公开许可文件和法庭记录的数据融合起来，以更好地检测可疑活动并指导资源有限的拦截工作。研究结果将使反人口贩运工作现代化，以跟上人口贩运者使用的复杂策略的步伐。 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为政策提供信息的新需求。这项研究将使用从深层和开放网络抓取数据的现有大型数据库，为自动检测非法业务建立风险评分。 风险评分是线性分类模型，只需要用户添加、减去和乘以几个小数字即可进行预测，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显着特征，例如数据混淆、非随机拼写错误、词汇表外和不常见单词的出现率高以及频繁使用 Unicode 字符，这使得自然语言处理变得困难。 风险评分学习问题被表述为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先进技术，并将产生可扩展的分支剪切过程来解决大型训练集的学习问题。它将采用半监督学习方法来使用标记和未标记数据来生成更好的风险评分。风险评分的绩效评估将根据合法和非法按摩企业的真实数据进行。研究结果将推广到不同的数据平台，这项工作中开发的方法预计将可应用于其他领域的人口贩运检测。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。

项目成果

Non-traditional cyber adversaries: Combating human trafficking through data science

非传统网络对手：通过数据科学打击人口贩运

• 发表时间: 2020
• 期刊: Cyber security
• 作者: Borrelli, Danielle;Caltagirone, Sherrie
• 通讯作者: Caltagirone, Sherrie

Interpretable models for the automated detection of human trafficking in illicit massage businesses

自动检测非法按摩行业人口贩运的可解释模型

• DOI: 10.1080/24725854.2022.2113187
• 发表时间: 2022
• 期刊: IISE Transactions
• 影响因子: 2.6
• 作者: Tobey, Margaret;Li, Ruoting;Özaltın, Osman Y.;Mayorga, Maria E.;Caltagirone, Sherrie
• 通讯作者: Caltagirone, Sherrie

Detecting Human Trafficking: Automated Classification of Online Customer Reviews of Massage Businesses

检测人口贩运：按摩企业在线客户评论的自动分类

• DOI: 10.1287/msom.2023.1196
• 发表时间: 2023
• 期刊: Manufacturing & Service Operations Management
• 作者: Li, Ruoting;Tobey, Margaret;Mayorga, Maria E.;Caltagirone, Sherrie;Özaltın, Osman Y.
• 通讯作者: Özaltın, Osman Y.