EAGER: ISN: Advanced Analytics, Intelligence and Processes for Disrupting Operations of Illicit Supply Networks

EARLY概念探索性研究补助金（EAGER）将通过研究非法供应链网络如何运作，特别是在使用法律的网络的流程和做法时，为促进国家繁荣和经济福利做出贡献。识别假冒和其他欺诈方的运作方式将有助于纠正美国经济数十亿美元的流失，这导致受影响的合法方的收入损失和美国政府的税收损失。假冒商品供应链利用了合法的分销网络，但由于其交易签名与法律的交易签名不同，因此是可验证的。 该项目涉及比较收集的物理库存和网络活动的分析，以确定将其与合法贸易区分开来的非法活动模式。这些模式来自销售数据、劳动力分析以及与时间和地点的预期报告贸易价值的差异。 通过有效地描述可疑的数字交易并更好地区分合法和非法企业，这项研究将在大多数商业企业使用的数字空间中产生更有效的对策。 该项目团队涉及计算机科学，操作工程和法医材料科学的跨学科专业知识，并将为研究生提供机会，在这个多方面的努力。 该项目将提供多层次的网络物理过程，以便在侦查的基础上发现非法活动及其促成非法网络。出发点是确定非法贸易为提高效率和模拟合法性所依赖的合法供应链的统计相关偏离情况。物理和网络取证过程将被调查和比较：混合机器学习和公共信息分析，如网络销售和产品定价网站，以及特权信息，如预计和实现的区域和季节性销售，使分析能够识别非法供应链中最突出的威胁表面点。要从根本上了解非法网络的运作-探索、揭露和利用其脆弱性-就需要收集证据以追溯源头。在物理空间中，该项目将研究使用与快速进入信息源（退货中心，秘密购物，在线活动）相关的成像推动的取证服务（IFFS），并能够确定是否符合序列化，防复制和防篡改。这些物理取证用于为专注于销售、定价和供应链分析的在线分析提供“独立核算”。 根据非法活动的迹象，该项目将评估如何引导潜在的非法用户根据他们复制合法供应链的能力来揭示自己是非法的。该奖项反映了NSF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 EArly-concept Grant for Exploratory Research (EAGER) will contribute to the advancement of national prosperity and economic welfare by studying how illicit supply chain networks operate, and in particular when using the processes and practices of legal networks. Identifying the ways in which counterfeiting and other fraudulent parties operate will help redress this multi-billion-dollar drain on the US economy, which results in both loss of income for the affected legitimate parties and loss of tax income for the US government. Counterfeit goods supply chains take advantage of legitimate distribution networks but are discoverable because their transactional signatures are different from those of legal transactions. This project involves comparing the analytics collected for both physical inventory and cyber activity to determine the patterns of illicit activities that distinguish them from those of lawful trade. These patterns arise from sales figures, labor analytics, and differences from expected reported trade values in time and locations. By effectively characterizing suspicious digital transactions and better distinguishing between legitimate and illegitimate enterprises, this research will lead to more effective countermeasures in the digital space used by the majority of commercial enterprises. The project team involve cross-disciplinary expertise in computer science, operations engineering, and forensic materials science, and will provide opportunities for graduate students in this multi-faceted effort. This project will provide the multi-tiered cyber-physical processes for statistical-based discovery of illicit activities and their enabling illicit networks. The starting point is determining statistically relevant deviations from the licit supply chains that illicit trade is dependent on for efficiency and for simulating legitimacy. Physical and cyber forensic processes will be investigated and compared: hybrid machine learning and analytics of public information such as web sales and product pricing sites, and privileged information such as projected and realized regional and seasonal sales, allow the analytics to identify the most salient threat surface points in the illicit supply chains. Fundamental understanding of the operations of illicit networks - to explore, expose and exploit their vulnerabilities - involves the collection of evidence to lead back to the source. In the physical space, this project will investigate the use of an imaging-fueled forensic service (IFFS) tied to fast-onramp sources of information (returns centers, secret shopping, online activity) and capable of determining compliance with serialization, copy prevention, and anti-tamper. These physical forensics are used to provide "independent accounting" for the on-line analytics focused on sales, pricing, and supply chain analytics. Based on indications of illicit activity, the project will evaluate means to steer potentially illicit users into revealing themselves as non-legitimate based on their ability to reproduce the legitimate supply chain.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.