The tool uses decades of data on the behavior of 30 major terrorist organizations, including Hezbollah and Hamas, to predict future behavior from past actions and events.
Terror prediction isn't quite like weather prediction. There are no warnings about widely scattered showers of shrapnel or heavy bombing, tapering off toward evening. Rather, counterterror data mining is being used to suggest high-level strategies for dealing with major terrorist organizations.
At the University of Maryland's Institute for Advanced Computer Studies (UMIACS), researchers have developed the SOMA (Stochastic Opponent Modeling Agents) Terror Organization Portal, or STOP, to help predict how terror groups will act and to share data through social networking.
"What it does is it takes a situation that someone might hypothesize or might be true, and tries to predict how a group might act based on what has actually happened," said V.S. Subrahmanian, computer science professor and UMIACS director. "We actually achieved accuracy on the order of about 90-plus percent ... but the predictions are somewhat coarse grained."
STOP won't tell any of the four defense agencies using the system that an attack will take place tomorrow, said Subrahmanian. But it will show that a specific group is likely to, say, increase its reliance on suicide attacks under certain conditions.
"It's more useful as a statistical tool to tell what a group will do," said Subrahmanian.
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