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                            Funding & Awards
 Using Artificial Intelligence Technologies to Expose Darknet Opioid Traffickers
Award Information
Awardee
              WEST VIRGINIA UNIVERSITY RESEARCH CORPORATION
Award #
              2018-75-CX-0032
Funding Category
              Competitive Discretionary
Location
                    MORGANTOWN, WV
Congressional District
              2
Status
              Past Project Period End Date
Funding First Awarded
              2018
Total funding (to date)
              $985,950
Original Solicitation
              NIJ FY18 Artificial Intelligence Technology Applied Research and Development for Law Enforcement Applications
Description of original award (Fiscal Year 2018, $985,950)
The crime of opioid trafficking - an illicit trade that involves the importation, manufacturing, cultivation, distribution, and sale of opioids - has co-evolved along with the advance of modern technologies. As the Internet has become one of the most important drivers behind the worldwide economy, it also provides an open and shared platform of making illicit profits at a lower degree of risk. Besides anonymity, the variety of available products and the ease of stealth delivery have made the option of trading illicit drugs via crypto-markets hosted on the dark web (i.e., a hidden part of the Internet) more desirable than conventional street-based markets. The crypto-markets on the dark web (a.k.a. eBay of drugs or Amazon of drugs) have posed new challenges to law enforcement agencies, such as how to address the growing online participation in opioid trafficking and sales is one of the most urgent tasks.
There is a lack of insight into the online ecosystem of opioid trafficking; in particular, there has not been any work linking participants on the dark web (e.g., crypto-markets such as Silk Road 3.1 and Dream Market) to the Internet (e.g., online social media such as Twitter) or the physical world. Utilizing Artificial Intelligence (AI) technologies to discover knowledge and recognize patterns from online data across dark web and Internet could provide valuable investigative leads, which might greatly facilitate law enforcements preventing, responding to, and disrupting the networks of opioid trafficking.
This  project will design and develop an intelligent system (named AlphaDetective) leveraging the latest advances in artificial intelligence technologies to automate the analysis of these data and link participants on the dark web to the Internet to provide timely investigative leads to law enforcements in the U.S. Those leads can be exploited to profile opioid traffickers, collect forensic evidence against opioid traffic ring, and disrupt the networks of opioid trafficking. The developed system may also applicable for other law enforcement applications, such as combating gang violence and human trafficking.
"Note: This project contains a research and/or development component, as defined in applicable law," and complies with Part 200 Uniform Requirements - 2 CFR 200.210(a)(14).
ca/ncf



  
Grant-Funded Publications
        
                      Fine-grained classification of drug trafficking based on Instagram hashtags
                  Heterogeneous Information Network Embedding With Adversarial Disentangler
                  Hyperbolic Graph Attention Network
                  Rep2Vec: Repository Embedding via Heterogeneous Graph Adversarial Contrastive Learning
                  Disentangled Dynamic Heterogeneous Graph Learning for Opioid Overdose Prediction
                  Deep Maximum a Posterior Estimator for Video Denoising
                  Accurate and Lightweight Image Super-Resolution With Model-Guided Deep Unfolding Network
                  Face spoofing detection under super-realistic 3D wax face attacks
                  Model-Guided Deep Hyperspectral Image Super-Resolution
                  Accelerating Convolutional Neural Network via Structured Gaussian Scale Mixture Models: A Joint Grouping and Pruning Approach
                  Survival neural networks for time-to-event prediction in longitudinal study
                  Large-scale Cost-aware Classification Using Feature Computational Dependency Graph

  
Date Created: August 31, 2018
    
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