AI-based Endpoint Defender (ABED)

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8750-18-C-0140
Agency Tracking Number: F173-002-0215
Amount: $149,967.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: AF173-002
Solicitation Number: 2017.3
Solicitation Year: 2017
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-04-30
Award End Date (Contract End Date): 2019-04-30
Small Business Information
153 Brooks Road, Rome, NY, 13441
DUNS: 085851181
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Daniel Scofield
 (315) 336-3306
Business Contact
 Nicole Capodiferro
Phone: (315) 336-3306
Research Institution
Existing endpoint defense solutions often rely on file signature blacklists and patterns derived from static analysis. These techniques, while proficient at mitigating the specific threats they are designed to detect, offer little protection against modern file and document formats that allow for the embedding of arbitrary data.AIS proposes ABED to detect and prevent malware and exploits in less than five minutes by extending upon proven Machine Learning enabled exploit and malware detection techniques previously developed by AIS under Air Force contract TAFFI (FA8750-15-C-0017). By leveraging existing research, ABED will have out-of-the-box capability to detect threats embedded in various common file types that can be used as a foundation for endpoint defense.ABED research will focus both on improving the pre-existing TAFFI detector, and on using it to build an enterprise ready endpoint defense solution. We will develop a self-learning and continuous training capability for the detector, allowing it to quickly learn how to detect malware and exploits in new file formats. This self-learning detector will serve as the foundation of our approach, and around it we will design an endpoint defender that can use scalable enterprise computing resources when available but which can still defend endpoints in their absence.

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

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