Defense Advanced Research Projects AgencyTagged Content List

Algorithms

A process or rule set used for calculations or other problem-solving operations

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The goal of the Modeling Adversarial Activity (MAA) program is to develop mathematical and computational techniques for modeling adversarial activity for the purpose of producing high-confidence indications and warnings of efforts to acquire, fabricate, proliferate, and/or deploy weapons of mass terror (WMTs). MAA assumes that an adversary’s WMT activities will result in observable transactions.
Computational capability is an enabler for nearly every military system, but increases in this capability are limited by available system power and constraints on the ability to dissipate heat. This is a challenge for embedded applications such as soldier-borne applications, UAVs and command and control systems on submarines. Today’s intelligence, surveillance and reconnaissance (ISR) systems have sensors that collect far more information than they can process in real time; as a result, what could be invaluable real-time intelligence data in the hands of our warfighters is simply discarded, or perhaps recorded and processed hours or days after it was collected.
From phony news on Web sites to terrorist propaganda on social media to recruitment videos posted by extremists, conflict in the information domain is becoming a ubiquitous addition to traditional battlespaces. Given the pace of growth in social media and other networked communications, this bustling domain of words and images—once relegated to the sidelines of strategic planning—is poised to become ever more critical to national security and military success around the globe.
As new defensive technologies make old classes of vulnerability difficult to exploit successfully, adversaries move to new classes of vulnerability. Vulnerabilities based on flawed implementations of algorithms have been popular targets for many years. However, once new defensive technologies make vulnerabilities based on flawed implementations less common and more difficult to exploit, adversaries will turn their attention to vulnerabilities inherent in the algorithms themselves.
In a target-dense environment, the adversary has the advantage of using sophisticated decoys and background traffic to degrade the effectiveness of existing automatic target recognition (ATR) solutions. Airborne strike operations against relocatable targets require that pilots fly close enough to obtain confirmatory visual identification before weapon release, putting the manned platform at extreme risk. Radar provides a means for imaging ground targets at safer and far greater standoff distances; but the false-alarm rate of both human and machine-based radar image recognition is unacceptably high. Existing ATR algorithms also require impractically large computing resources for airborne applications.