Novel Detection Mechanisms for Advanced Persistent Threats

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00039-15-C-0028
Agency Tracking Number: O123-IA4-2093
Amount: $499,354.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: OSD12-IA4
Solicitation Number: 2012.3
Timeline
Solicitation Year: 2012
Award Year: 2015
Award Start Date (Proposal Award Date): 2015-09-30
Award End Date (Contract End Date): 2016-09-29
Small Business Information
4850 Hahns Peak Drive, Suite 200, Loveland, CO, 80538
DUNS: 956324362
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Randy Paffenroth
 Program Director
 (970) 461-2000
 randy.paffenroth@numerica.us
Business Contact
 Emily Poore
Title: Technical Point of Contact
Phone: (970) 461-2000
Email: emily.poore@numerica.us
Research Institution
N/A
Abstract
The Department of Defense (DoD) is supported by a vast global network of computers, sensors, and equipment that is continually at risk of being breached by adversaries. Such cyber elements comprise an important part of the DoDs military readiness and the loss or degradation of such elements would reduce key advantages in communication, intelligence, and organization. Despite heavy investments in security and cyber defense, the sheer ubiquity and interconnectedness of DoD equipment leave open the possibility of intrusion through a myriad of means including advanced persistent threats (APTs). Such threats take many forms, including Trojans, back-doors in embedded systems, worms, spear-phishing, and viruses, all of which could prove detrimental to the war fighter if not discovered. As part of our work we have demonstrated several novel ideas for detecting APTs based upon modern ideas in space-time signal processing, multiple hypothesis testing, and robust principal component analysis. In particular, previous results by Numerica have proven especially pertinent to APT detection since these algorithms have been demonstrated to scale to millions of data streams, can fuse data from a variety of input types, and have quite advantageous sparsity properties for visualization and analytics.

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

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