UMD Researchers Develop New Methods to Combat Terrorist Group

Researchers at the University of Maryland are analyzing data to predict patterns and plots of terrorist activity. While studying the Pakistani terrorist group Lashkar-e-Taiba (LeT), the perpetrators of the November 2008 assault on Mumbai, India, an interdisciplinary research team at UMD’s Laboratory for Computational Cultural Dynamics determined that combating future attacks from the group requires a cocktail of actions, including fostering dissent within LeT, hampering the organization's ability to conduct communication campaigns or provide social services, and disrupting the links between LeT and other Islamist terror groups.

The UMD researchers came to this conclusion after analyzing 20 years’ worth of data and around 770 different variables regarding Lashkar-e-Taiba (LeT), searching for trends in attacks, such as the 2008 bombings and shootings in Mumbai. The algorithms the researchers used are similar to those of powerful websites such as Google.com and Amazon.com that predict consumer trends and effective advertising. The data mining suggests that the most effective ways to stop violent threats are to create dissension within terror groups, to promote government restrictions on activity and to hinder the group’s attempts at recruitment. These methods are proposed to have more effective results than capturing or attacking the group's leaders, such as the recent killing of al-Qaida’s number two leader. This information is crucial because it can help improve counter-terrorism efforts and to gain insight into decision-making techniques to predict future attacks.

Lashkar-e-Taiba was chosen to be analyzed since it has been studied less than other groups and seemed to be turning into a larger threat. The group originated in the 1980’s, aiming to spread a version of Islam over a disputed area of India and promoting violent jihad, or holy war. The software generated hundreds of trends about a vast range of LeT attacks including their targeting of civilians, professional security forces, transportation centers, security installations, and symbolic/tourist locations. Even though Lashkar-e-Taiba has held a low-profile for the past few years, this group is still a threat. Data mining based on past events is imperfect since terrorist groups generally try to implement an element of surprise into their attacks.

"Our study of LeT is different," explains Professor V.S. Subrahmanian, lead author of the study and director of the University of Maryland's Laboratory for Computational Cultural Dynamics. "It is the first in-depth analysis of a terror group that uses sophisticated data mining algorithms to learn temporal probabilistic rules as well as new algorithms to automatically suggest set policies which are sets of actions that should and should not be taken in order to elicit a desired behavior. Companies like Google and Amazon use these kinds of analytic methodologies to model the behaviors of customers every day. Decision-makers dealing with deadly threats to national security should have the same kinds of tools available."

For more information, visit:

http://www.newsdesk.umd.edu/scitech/release.cfm?ArticleID=2772

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September 10, 2012