Automated Asymmetric Red Teams (AART)

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-07-C-0929
Agency Tracking Number: O074-005-4017
Amount: $99,992.00
Phase: Phase I
Program: STTR
Awards Year: 2007
Solicitation Year: 2007
Solicitation Topic Code: OSD07-T005
Solicitation Number: N/A
Small Business Information
REFERENTIA SYSTEMS, INC.
550 Paiea Street, Suite #236, Honolulu, HI, 96819
DUNS: 176527471
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: Y
Principal Investigator
 Steve Upton
 Senior Computer Scientist
 (808) 423-1900
 supton@referentia.com
Business Contact
 Nelson Kanemoto
Title: President & CEO
Phone: (808) 423-1900
Email: kane@referentia.com
Research Institution
 UNVERSITY OF HAWAII
 Sun-Ki Chai
 2500 Campus Road
Honolulu, HI, 96822
 (808) 956-7234
 Nonprofit college or university
Abstract
Red teaming is a technique that has been used successfully for some time in the military community to uncover system vulnerabilities or to find exploitable gaps in operational concepts, such as new weapon systems or tactics. Red teaming is currently a human intensive technique that typically brings together experts relevant to the system under consideration and who are then charged with identifying weaknesses. The primary challenge in this process is overcoming the limited exploration of possibilities and the potential bias of human experience and these limitations will likely hold for most adversary models that humans would build. A significant opportunity exists in automatically generating realistic adversary models that could potentially be used in any DoD simulation-based system, whether the goal of that simulation-based system is training, analysis, acquisition, or test and evaluation. This tool could also be used in homeland security and law enforcement, where adversaries in this case could be any agent out to do us harm. The overall objective of this effort is to research and design an asymmetric adversarial behavioral generation system (AART) that can provide computer-generated adversaries using asymmetric tactics and strategies in synthetic training environments. The following are specific technical objectives for this research.

* information listed above is at the time of submission.

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