Crowdsourcing intelligence to combat terrorism: harnessing bottom-up collection to prevent lone-wolf terror attacks
Coultas, Bryan T.
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U.S. officials have acknowledged that attackers of the lone-wolf and isolated-cell organizational type are on the rise and now pose a greater threat than major coordinated actions. Traditional intelligence methods, using a top-down approach with an emphasis on signals intelligence, are ill-equipped to identify and prevent terrorists using lone-wolf tactics. Crowdsourcing, as a problem-solving technique, is a relatively new idea but has shown great promise in tackling issues similar to the identification of lone-wolf terrorists. At its core, crowdsourcing is a method for thousands or even millions of people to contribute their knowledge, expertise, or skills towards a unified task. Done correctly, it has produced results unachievable by traditional tasking of humans or computers. This thesis identifies how the signals surrounding lone-wolf attacks are different and more subtle in nature from those mounted by organized terror groups. In turn, the thesis examines the potential benefits of crowdsourcing intelligence in order to strengthen the U.S. intelligence community’s ability to approach this emerging problem of lone-wolf terrorism. In short, this thesis proposes that the U.S. intelligence community harness the power of U.S. citizens to help prevent identify the subtle indictors presented by lone-wolf terrorists in order to prevent lone-wolf terrorist attacks.
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