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ALCHEMI: Attacker Learning in Cybernetworks using Heterogeneous Energy-guided Model Inference

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-19-C-0352
Agency Tracking Number: N19A-021-0130
Amount: $140,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N19A-T021
Solicitation Number: 19.A
Timeline
Solicitation Year: 2019
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-06-03
Award End Date (Contract End Date): 2019-12-09
Small Business Information
12 Gill Street Suite 1400
Woburn, MA 01801
United States
DUNS: 967259946
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Nathan Schurr Nathan Schurr
 Principal Scientist
 (781) 935-3966
 nschurr@aptima.com
Business Contact
 Thomas McKenna
Phone: (781) 496-2443
Email: brouady@aptima.com
Research Institution
 University of South Florida
 Sara Labadie-Siville Sara Labadie-Siville
 
4019 E. Fowler Ave Suite 100
Tampa, FL 33617
United States

 (813) 974-5431
 Nonprofit College or University
Abstract

The United States relies on networks of cyber-physical systems to conduct military and commercial operations, such as logistics, transportation, information sharing, energy production and distribution, financial transactions, elections, and infrastructure management. As the volume and diversity of cyber-attacks on these networks dramatically increase, there is a growing need for advanced tools and techniques to defend these networks. With this in mind, Aptima, Inc. and its academic partner at the University of South Florida, Prof. Xinming (Simon) Ou, propose to develop ALCHEMI: Attacker Learning in Cybernetworks using Heterogeneous Energy-guided Model Inference. ALCHEMI will provide operators supporting Defensive Cyber Operations (DCOs) with tools that automate the data collection and analysis of network observables. With a novel integration of machine learning, artificial intelligence, and dynamic attack graph technologies ALCHEMI will provide a suite of functionalities enabling visualization of both the current state of computer networks and the threats against it, as well as inferences regarding the goals of the attackers.

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

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