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Topological Anomaly Detection

Award Information
Agency: Department of Defense
Branch: Special Operations Command
Contract: H9240523P0001
Agency Tracking Number: S224-007-0030
Amount: $149,867.34
Phase: Phase I
Program: SBIR
Solicitation Topic Code: SOCOM224-007
Solicitation Number: 22.4
Solicitation Year: 2022
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-03-02
Award End Date (Contract End Date): 2023-09-08
Small Business Information
2460 Garden Road, Suite E
Monterey, CA 93940-5398
United States
DUNS: 828255344
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Brad Morris
 (831) 498-9445
Business Contact
 Michael Douglass
Phone: (571) 262-9853
Research Institution

We propose a study to assess the feasibility and efficacy of using graph/based analytics and Graph Neural Networks (GNN) to identify anomalous individual and organizational personas in financial transaction datasets. Currently, there is an urgent and expensive need to deny nefarious transnational state and non/state actors from accessing global financial systems, export/controlled technologies, critical supply chain networks, and other sensitive systems pertinent to U.S. national security. While various techniques exist today that perform pattern recognition and anomaly detection reporting, these techniques often result in limited operational value for key stakeholders and decision makers. As such, our analysis intends to quantify the impact of automated graph/based anomaly detection techniques for providing timely, relevant, actionable reporting in the financial threat intelligence domain. This Phase I Small Business Innovation Research (SBIR) proposal presents our research plan that aims to assess these objectives to determine future courses of action and best practices to achieve automated graph/based generative/alert reporting and analyst/defined discovery in databases. Finally, our research intends to provide a Technical Design Document (TDD) that holistically examines how graph/based techniques may be applied across SOCOM’s net/new (Greenfield) and legacy modified (Brownfield) systems.

* Information listed above is at the time of submission. *

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