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

Author

Chen, Hsinchun

Author_Institution

Dept. of Manage. Inf. Syst., Arizona Univ., Tucson, AZ

fYear

2008

fDate

17-20 June 2008

Firstpage

94

Lastpage

97

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/ISI.2008.4565036

Filename

4565036