D-ISN: TRACK 1: Collaborative Research: An Interdisciplinary Approach to Understanding, Modeling, and Disrupting Drug and Counterfeit Illicit Supply Chains

New York University, New York NY

Investigators

Abstract

The objective of this five-year Disrupting Operations of Illicit Supply Networks (D-ISN) study is to understand the operations of illicit actors in the cyberworld, in both the open web and dark web, and the supply chains and payment systems for online drug sales, counterfeit pharmaceuticals, and goods, including Personal Protective Equipment (PPE). It seeks to catalyze game-changing technological innovations by creating tools and supply chain models to improve discovery and traceability of illicitly sourced products and identify effective disruption strategies. These supply chains will be studied from source to delivery through the money laundering of profits. The results will be informed by datasets drawn from open and dark websites and from data made available by industrial collaborators. The project will advance our national ability to counter malicious activities in the cyberworld and social media innovative approaches using mathematical models, supply chain analytics and computer science for the detection and disruption of drug and counterfeit supply chains. The project uses a multidisciplinary set of methods which include data analysis, mathematical modeling, and ethnographic approaches to advance our understanding of online drug and counterfeit supply chains and how to disrupt them. Specially, this project will address three major goals in combating drug and counterfeit illicit supply chains: (1) understanding and detecting the illicit trade patterns quantitatively and qualitatively by using data from the payment processing, hosting, underground communications and court cases; (2) constructing a description of the supply chain that can then be modeled using appropriate techniques such as non-cooperative game theory framework to study different disruption strategies; and (3) studying the possibility of different disruption strategies that could be implemented by government, corporate and multilateral actors. The project will integrate advanced automated data collection and analysis tools, and sociological analysis of the illicit trafficking networks, and adversarial game theory frameworks. The project team's collaboration with industry and discussions with law enforcement agencies will facilitate an interactive process that can fine-tune disruption techniques and suggest pragmatic real-world implementation strategies and policy recommendations. 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.