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N.Y. bomb plot highlights limitations of data mining
Like weather forecasting, data mining can predict major storms but not where each raindrop will fall
Computerworld - 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.
Effectiveness questioned
That fact is likely to provide more fodder for those who question the effectiveness of using data mining approaches to uncover and forecast terror plots. Since the terror attacks of Sept. 11, the federal government has spent tens of millions of dollars on data mining programs and behavioral surveillance technologies that are being used by several agencies to identify potential terrorists.
The tools work by searching through mountains of data in large databases for unusual patterns of activity, which are then used to predict future behavior. The data is often culled from dozens of sources, including commercial and government databases, and meshed together to see what kind of patterns emerge.
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Computerworld - 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.
Effectiveness questioned
That fact is likely to provide more fodder for those who question the effectiveness of using data mining approaches to uncover and forecast terror plots. Since the terror attacks of Sept. 11, the federal government has spent tens of millions of dollars on data mining programs and behavioral surveillance technologies that are being used by several agencies to identify potential terrorists.
The tools work by searching through mountains of data in large databases for unusual patterns of activity, which are then used to predict future behavior. The data is often culled from dozens of sources, including commercial and government databases, and meshed together to see what kind of patterns emerge.
Read Full Story
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Comment by Will on May 5, 2010 at 11:23pm -
Actually, it looks like something had raised suspicion with authorities: in 2004, a man who purchased real estate from Shahzad was interviewed by detectives from the Joint Terrorism Task Force (see http://www.nytimes.com/2010/05/05/nyregion/05profile.html?bl). Whether this red-flag was raised byany type of data-mining remians to be seen.
However, I wouldn't be as pessimistic as the author leads us to beleive. The facts in this case actually higlight the value that data-centric approach can add to our defense against acts of terrorism. What initial reporting has revealed is that Shahzad was not a conspiculously suspicious person; he seemed to have the trust of neighbors. landlords, etc. This means, with the traditional methods we rely on to catch criminals, making judgement at a personal level have a very low chance of ever thrawting a man like this. However, a terror-scoring model could (and should have in this case) made a connection when a man who 1. had just returned from an extended stay in Pakistan, and 2. whose personal finances took a sudden and precipitous decline and, starts to 3. buy multiples bags of fertilizer, and propane. All very common events and unlikely to indicate terrorism individually, but much rarer, and more threatening together. In this way, modelling info from multiple databases will be able to give a more complete picture of risks, more than any one person would be witness to.
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