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

基本信息

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

项目摘要

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确定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。尽管许多合法企业都使用在线平台，例如广告服务，招聘广告和审查委员会，但非法业务也可以利用这些服务，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，以促进对非法业务的检测。与全球解放网络合作，该数据分析非营利组织致力于反对人口贩运，该项目将融合特定于商业运营的数据，并将数据与公开可用的许可文件和法院记录中的数据融合在一起，以更好地检测可疑活动并指导资源构成的犯罪工作。结果将使反贩运的努力现代化，以与贩运者使用的复杂策略保持同步。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兴需求，以告知政策。使用深处和开放网络中的大量刮擦数据数据库，这项研究将建立自动检测非法业务的风险评分。风险分数是线性分类模型，仅需要用户添加，减去和乘以几个小数，以进行预测，因此，这些模型易于应用和理解。非法企业中的广告中的信息具有区别特征，例如数据混淆，非随机拼写错误，多次播放外词和不寻常的单词的出现，以及频繁使用Unicode字符，从而使自然语言处理变得困难。风险分数学习问题被提出为非线性混合企业优化问题。分析框架利用并从优化和统计学习中扩展了最新技术，并将产生可扩展的分支和切割程序，以解决大型培训集中的学习问题。它将采用半监督学习方法使用标记和未标记数据来产生更好的风险分数。风险评分的绩效评估将由合法和非法按摩业务的真实数据告知。研究结果将可以推广到不同的数据平台，预计这项工作中开发的方法将可以翻译成其他部门的人口贩运。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准通过评估来获得支持的。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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

DOI：
发表时间：
2020
期刊：
Cyber security
影响因子：
0
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
影响因子：
0
作者：
Li, Ruoting;Tobey, Margaret;Mayorga, Maria E.;Caltagirone, Sherrie;Özaltın, Osman Y.
通讯作者：
Özaltın, Osman Y.