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IEDs in the Dark Web: Genre classification of improvised explosive device web pages

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1 Author(s)
Hsinchun Chen ; Artificial Intelligence Laboratory, Department of Management Information Systems, University of Arizona, Tucson, 85721 USA

Improvised explosive device web pages represent a significant source of knowledge for security organizations. These web pages exist in distinctive genres of communication, providing different types and levels of information for the intelligence community. This paper presents a framework for the classification of improvised explosive device web pages by genre. The approach uses a complex feature extractor, extended feature representation, and support vector machine learning algorithms. Improvised explosive device web pages were collected from the Dark Web and two classification models were examined, one using feature selection. Classification accuracy exceeded 88%.

Published in:

Intelligence and Security Informatics, 2008. ISI 2008. IEEE International Conference on

Date of Conference:

17-20 June 2008