Military Embedded Systems

DARPA adds network-analysis tools to its antiterrorism arsenal


January 30, 2018

Lisa Daigle

Assistant Managing Editor

Military Embedded Systems

DARPA adds network-analysis tools to its antiterrorism arsenal
Image courtesy HRL Laboratories.

MALIBU, Calif. Research and development lab HRL Laboratories will develop a set of software tools -- to be known as the Complex Analytics of Network of Networks (CANON) -- for the Defense Advanced Research Projects Agency (DARPA) that is intended to detect and warn intelligence analysts about weapons of mass terrorism (WMT) activity.


CANON will use integrated information from networks of massive amounts of intelligence data to find WMT activity at a level surpassing today’s best practices novel mathematical frameworks and techniques as part of its "Modeling Adversarial Activity" (MAA) program to track down activities related to WMTs.

WMT-related activity, while often hidden, can sometimes be traced by its electronic trail, which can be spread across many online domains and in many contexts. This kind of adversarial activity is nearly impossible to detect within isolated networks, say HRL Labs officials, but becomes detectable and recognizable when networks are analyzed together.

“WMTs can be known types of destructive weapons, but they can also be improvised from materials that are not alarming when purchased alone, such as the components of the bombs set off at the 2013 Boston Marathon. Despite being relatively small homemade munitions, the resulting atmosphere of terror they created has yet to dissipate at such public events,” said Jiejun Xu, HRL’s principal investigator for the MAA program.

The DARPA research requires that the tool create a unified world view with high accuracy that is also scalable to a size that involves a massive ten billion nodes; for this portion, the HRL researchers will use a technique called network alignment. “Once we have our world view developed, we then must find the needle in the haystack,” Xu said. “We use that metaphor to describe a technique called subgraph matching, robust and efficient graph-based search algorithms that identify WMT pathways hidden in the world view graph. When we search our world view network and rank the indicators for these pathways, intelligence analysts can then use them to accurately lead back to the groups or individuals who are creating the WMT concern.”

Arizona State University is partnering with HRL Labs in the MAA program.





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