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Tactical Immune System based on Dynamically Assigned Sense of Self

Award Information
Agency: Department of Defense
Branch: Army
Contract: W56KGU-16-C-0064
Agency Tracking Number: A16A-010-0025
Amount: $149,790.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: A16A-T010
Solicitation Number: 2016.0
Timeline
Solicitation Year: 2016
Award Year: 2016
Award Start Date (Proposal Award Date): 2016-08-26
Award End Date (Contract End Date): 2017-03-01
Small Business Information
13785 Laurel Rock Drive
Clifton, VA 20124
United States
DUNS: 055099483
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Xinyuan Wang
 (703) 830-6298
 xwang5_97@yahoo.com
Business Contact
 Xinyuan Wang
Phone: (703) 830-6298
Email: xwang5_97@yahoo.com
Research Institution
 George Mason University
 Eileen Gallagher
 
4400 University Drive
Fairfax, VA 22030
United States

 (703) 993-2292
 Nonprofit College or University
Abstract

Inspired by the self-nonself discrimination in our natural immune system, we propose to develop the first practical immune system that will provide unprecedented real-time protection for identified tactical platforms and networks in ARMY's Common Operating Environment (COE). Unlike previous models of self which are passive reflection of the exiting being of the protected program or system, our model of self is active in that it dynamically assign a unique mark to the protected program and system. Such a dynamically assigned sense of self eliminates training and re-alignment needed by other models of self and enables us to effectively and efficiently detect and block the first nonself system call of any control flow hijacking attack with no false positive. Such an unprecedented capability further enables us to accurately capture the system call sequence right before and after the detected cyberattack as well as the memory buffer that contains the attack code. Our proof-of-concept prototype in Linux has successfully immunized real-world, unpatched, vulnerable applications (e.g., Snort 2.6.1 with over 140,000 lines of C code) against otherwise working exploits with no more than 4% overall run time performance overhead.

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

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