In order to combat asymmetric threats like terrorism, security analysts need more than piles of data. It takes a network to fight a network.

So keeping this in mind, researchers at the University of Maryland's Institute for Advanced Computer Studies (UMIACS) have developed software that allows faster analysis and forecast of potential terrorist behavior as well as allows networking with other analysts. Called the "SOMA Terror Organization Portal (STOP)", the project is headed by V S Subrahmanian, an Indian origin Computer Science Professor at UMIACS.

SOMA (Stochastic Opponent Modeling Agents) is a formal, logical-statistical reasoning framework that uses data about past behavior of terror groups in order to learn rules about the probability of an organization, community, or person taking certain actions in different situations.

In collaboration between computer scientists and political scientists, SOMA has generated tens of thousands of rules about the likely behavior of each of around 30 terrorist groups, including major terrorist outfits such as Hezbollah, Hamas, and Hezb-I-Islami.

In addition to offering accurate behavioral models and forecasting algorithms, STOP can act as a virtual roundtable for terrorism experts to gather around and form a rich community that transcends artificial boundaries.

Funded by the Air Force Office of Scientific Research, STOP currently has users from four defense agencies. The users, in addition to performing queries and running a prediction engine, can mark rules as useful or not useful and leave comments about the rules.

According to Aaron Mannes, UMIACS researcher and author of "Profiles in Terror: The Guide to Middle East Terrorist Organizations," analysts need to learn from other analysts. This system allows multiple users to arrive at a shared understanding of how a terror group operates, and what it might do in the future. Using the queries, analysts can examine the underlying data, and then, using the forecasting capabilities, test their theories, explained Mannes.