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N.y. Bomb Plot Highlights Limitations of Data Mining

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The recent failed bombing attempt in New York City shows the limitations of data-mining technology when used in security applications.

Since the terror attacks of Sept. 11, the U.S. government has spent tens of millions of dollars on data-mining programs that are used by agencies to identify potential terrorists. The Department of Homeland Security's Automated Targeting System assigns terror scores to U.S. citizens, and the Transportation Security Administration's Secure Flight program analyzes airline passenger data. However, it is unclear how effective these programs have been in identifying and stopping potential terrorist threats.

Using data mining to search for potential terrorists is similar to looking for a needle in a haystack, says BT chief security officer Bruce Schneier. "Data mining works best when there's a well-defined profile you're searching for, a reasonable number of attacks per year, and a low cost of false alarms," Schneier says. However, even the most accurate and finely tuned data-mining system will generate one billion false alarms for each real terrorist plot it uncovers, he says.

From Computerworld
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