SBIR Phase I: Illuminating dark web electronic commerce

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力将是打击假冒和非法商品的在线贸易。该项目将整合十年来对匿名在线（“暗网”）市场的学术研究成果，并将假冒药品在线销售建模与传统电子商务市场的新型监控解决方案相结合。它将允许通过试点客户测试开发和验证一个综合平台，用于自动连续收集和分析假冒和非法商品在线业务的主要参与者。通过自动化，拟议的技术应大大降低品牌保护管理人员（和执法部门）的成本，使他们能够更有效地利用有限的资源。这项工作还应有助于解决与假药等假冒产品扩散有关的一些紧迫的经济和公共卫生问题。这项小型企业创新研究（SBIR）第一阶段项目将展示许多手动在线假冒监测活动的自动化。该项目还将表明，直观的视觉界面可以帮助客户（执法机构，品牌保护经理）立即访问更高级别的对象，这些对象对调查目的更有用。这些更高级别的对象包括关于特定实体进行的销售量、供应商之间的重复数据删除或库存集群的指标。为此，该项目将使用研究实验室中的原型技术进一步开发自动分类和分析，将这些技术扩展到生产环境，以进一步减少人为干预，并将这些技术与针对略有不同的应用情况开发的新算法联合收割机相结合（传统电子商务市场）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进行评估，更广泛的影响审查标准。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be to combat online trade in counterfeit and illicit goods. The project will integrate the results of a decade of academic research on anonymous online ("dark net") marketplaces and modeling of counterfeit pharmaceutical online sales with novel monitoring solutions for traditional e-commerce marketplaces. It will allow for the development and validation through pilot customer tests of an integrated platform for automated continuous data collection and analysis of the major players in the counterfeit and illicit goods online business. Through automation, the proposed technology should considerably reduce costs to brand protection managers (and law enforcement), allowing them to use their limited resources more effectively. This work should also help address some pressing economic and public health issues linked to the proliferation of counterfeits, such as counterfeit drugs.This Small Business Innovation Research (SBIR) Phase I project will demonstrate automation of many manual online counterfeiting monitoring activities. The project will also show that intuitive visual interfaces can help customers (law enforcement agencies, brand protection managers) have immediate access to higher-level objects more useful for investigative purposes. These higher-level objects include metrics on the amount of sales conducted by a specific entity, deduplication between vendors, or inventory clustering. To do so, the project will further develop automated classification and analysis using techniques that were prototyped in the research lab, scale these techniques up to a production environment to further minimize human intervention, and combine these techniques with novel algorithms developed for slightly different application cases (traditional e-commerce marketplaces).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.