Title :
IEDs in the Dark Web: Genre classification of improvised explosive device web pages
Author_Institution :
Dept. of Manage. Inf. Syst., Arizona Univ., Tucson, AZ
Abstract :
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%.
Keywords :
Internet; national security; organisational aspects; support vector machines; IED; complex feature extractor; dark Web; extended feature representation; feature selection; genre classification; improvised explosive device Web pages; intelligence community; security organizations; support vector machine learning algorithms; Data mining; Explosives; Feature extraction; Information security; Learning systems; Machine learning; Machine learning algorithms; Support vector machine classification; Support vector machines; Web pages; dark web; genre classification; improvised explosive device;
Conference_Titel :
Intelligence and Security Informatics, 2008. ISI 2008. IEEE International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-2414-6
Electronic_ISBN :
978-1-4244-2415-3
DOI :
10.1109/ISI.2008.4565036
