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Self-Coding Cyber Fixes

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0860-21-C-7115
Agency Tracking Number: B2-3001
Amount: $1,509,997.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: MDA19-009
Solicitation Number: 19.2
Timeline
Solicitation Year: 2019
Award Year: 2021
Award Start Date (Proposal Award Date): 2020-12-16
Award End Date (Contract End Date): 2022-12-15
Small Business Information
591 Camino de la Reina Suite 610
San Diego, CA 92108-3108
United States
DUNS: 010681380
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 John Geddes
 (619) 398-1410
 jgeddes@ramlabs.com
Business Contact
 Maggie Sullivan
Phone: (619) 398-1410
Email: msullivan@ramlabs.com
Research Institution
N/A
Abstract

When deploying software to systems running in secure environments, it is of upmost importance that everything is done to ensure the software is secure and free of bugs and cyber vulnerabilities. While there exists a large number of tools that scan source code to find potential bugs and vulnerabilities, it is left to developers and subject matter experts (SMEs) to manually fix all of the identified issues. Not only is this a time-consuming process, it is made even worse by the large number of false positives, code that is actually bug free but still flagged by the tool as containing a vulnerability. To address this large technical gap, RAM Laboratories is proposing the Deep Learning for Precise, Automatic and Trusted Code Hardening and Error Removal (DL-PATCHER) solution. Leveraging recent state-of-the-art advances in deep learning, DL-PATCHER is able to use large and diverse code repositories to build neural network models that are able to reason over source code and automatically generate patches that fix bugs and vulnerabilities with astonishingly high accuracy rates. Approved for Public Release | 20-MDA-10643 (3 Dec 20)

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

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