Saturday’s botched bombing attempt in New York City provides an example of why the use of data mining approaches to uncover potential terrorism plots is a little like weather forecasting.
“You definitely need to do it, because it gives you warning of major storms,” said John Pescatore, an analyst with Gartner Inc. and a former analyst with the National Security Agency. “But it’s not going to tell you about individual raindrops.”
Faisal Shahzad, a naturalized U.S. citizen of Pakistani descent was arrested Monday at New York’s John F. Kennedy International airport in connection with an attempt to detonate a car bomb in Times Square. Shahzad, who is scheduled to be indicted on terrorism-related charges in Manhattan today, was pulled off a plane bound for Dubai, minutes before the jetliner was scheduled to take off.
Shahzad is alleged to have parked an explosives-laden vehicle in Times Square, apparently with the intention of blowing it up. Media reports quoting the FBI and other authorities said the bomb could have caused a substantial number of deaths and injuries had it detonated.
The anti-terrorism task force was quickly able to identify Shahzad as the prime suspect in the case thanks to a series of mistakes the would-be bomber made. But for the moment, there is little to show that authorities had any inkling of either Shahzad or of his plot beforehand.
via N.Y. bomb plot highlights limitations of data mining – Computerworld.
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