| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | April 4, 2022 |
| Latest Amendment Date: | December 22, 2023 |
| Award Number: | 2210091 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Dan Cosley
dcosley@nsf.gov (703)292-8832 CNS Division Of Computer and Network Systems CSE Direct For Computer & Info Scie & Enginr |
| Start Date: | May 1, 2022 |
| End Date: | April 30, 2024 (Estimated) |
| Total Intended Award Amount: | $298,284.00 |
| Total Awarded Amount to Date: | $330,284.00 |
| Funds Obligated to Date: |
FY 2024 = $16,000.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
700 S UNIVERSITY PARKS DR WACO TX US 76706-1003 (254)710-3817 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
One Bear Place #97360 Waco TX US 76798-7360 |
| Primary Place of Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
Special Projects - CNS, Secure &Trustworthy Cyberspace |
| Primary Program Source: |
01002425DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070, 47.075 |
ABSTRACT
Increasingly, people buy and sell goods and services directly from other people via online marketplaces. While many online marketplaces enable transactions among reputable buyers and sellers, some platforms are vulnerable to suspicious transactions. This project investigates whether it is possible to automate the detection of illegal goods or services within online marketplaces. First, the project team will analyze the text of online advertisements and marketplace policies to identify indicators of suspicious activity. Then, the team will adapt the findings to a specific context to locate stolen motor vehicle parts advertised via online marketplaces. Together, the work will lead to general ways to identify signals of illegal online sales that can be used to help people choose trustworthy marketplaces and avoid illicit actors. This project will also provide law enforcement agencies and online marketplaces with insights to gather evidence on illicit goods or services on those marketplaces.
This research assesses the feasibility of modeling illegal activity in online consumer-to-consumer (C2C) platforms, using platform characteristics, seller profiles, and advertisements to prioritize investigations using actionable intelligence extracted from open-source information. The project is organized around three main steps. First, the research team will combine knowledge from computer science, criminology, and information systems to analyze online marketplace technology platform policies and identify platform features, policies, and terms of service that make platforms more vulnerable to criminal activity. Second, building on the understanding of platform vulnerabilities developed in the first step, the researchers will generate and train deep learning-based language models to detect illicit online commerce. Finally, to assess the generalizability of the identified markers, the investigators will apply the models to markets for motor vehicle parts, a licit marketplace that sometimes includes sellers offering stolen goods. This project establishes a cross-disciplinary partnership among a diverse group of researchers from different institutions and academic disciplines with collaborators from law enforcement and industry to develop practical, actionable insights.
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.
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