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AMELIA - Automatic Machine Learning based Code Patching

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0860-20-C-7018
Agency Tracking Number: B192-009-0174
Amount: $100,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: MDA19-009
Solicitation Number: 19.2
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2019-11-25
Award End Date (Contract End Date): 2020-05-24
Small Business Information
15400 Calhoun Drive Suite 190
Rockville, MD 20855
United States
DUNS: 161911532
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Guang Jin
 Program Manager
 (301) 294-5221
Business Contact
 Mark James
Phone: (301) 294-5200
Research Institution

Intelligent Automation, Incorporated (IAI) proposes an Automatic Machine Learning based Code Patching (AMELIA) framework to generate effective and efficient source code level patches. AMELIA first converts source code written in different programing languages into a language/target independent Intermediate Representation (IR). The collected IR code is then fed into Deep Neural Networks (DNNs) to train advanced Machine Learning (ML) models which will be used to detect and localize vulnerabilities from complex software systems. AMELIA also uses the DNN based models to represent a uniform knowledge on security vulnerabilities. AMELIA generates source code level patches, and will use existing analysis/optimization tools to optimize the placement of generated patches. Approved for Public Release | 19-MDA-10270 (18 Nov 19)

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

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