Persistent Link:
http://hdl.handle.net/10150/106000
Title:
Automaticially Detecting Deceptive Criminal Identities
Author:
Wang, Gang; Chen, Hsinchun; Atabakhsh, Homa
Citation:
Automaticially Detecting Deceptive Criminal Identities 2004-03, 47(3):71-76 Communications of the ACM
Publisher:
ACM
Journal:
Communications of the ACM
Issue Date:
Mar-2004
Description:
Artificial Intelligence Lab, Department of MIS, Univeristy of Arizona
URI:
http://hdl.handle.net/10150/106000
Submitted date:
2004-08-17
Abstract:
Fear about identity verification reached new heights since the terrorist attacks on Sept. 11, 2001, with national security issues related to detecting identity deception attracting more interest than ever before. Identity deception is an intentional falsification of identity in order to deter investigations. Conventional investigation methods run into difficulty when dealing with criminals who use deceptive or fraudulent identities, as the FBI discovered when trying to determine the true identities of 19 hijackers involved in the attacks. Besides its use in post-event investigation, the ability to validate identity can also be used as a tool to prevent future tragedies. Here, we focus on uncovering patterns of criminal identity deception based on actual criminal records and suggest an algorithmic approach to revealing deceptive identities.
Type:
Journal Article (Paginated)
Language:
en
Keywords:
Artificial Intelligence; Data Mining
Local subject classification:
National Science Digital Library; NSDL; Artificial intelligence lab; AI lab; Criminal profile analysis

Full metadata record

DC FieldValue Language
dc.contributor.authorWang, Gangen_US
dc.contributor.authorChen, Hsinchunen_US
dc.contributor.authorAtabakhsh, Homaen_US
dc.date.accessioned2004-08-17T00:00:01Z-
dc.date.available2010-06-18T23:38:03Z-
dc.date.issued2004-03en_US
dc.date.submitted2004-08-17en_US
dc.identifier.citationAutomaticially Detecting Deceptive Criminal Identities 2004-03, 47(3):71-76 Communications of the ACMen_US
dc.identifier.urihttp://hdl.handle.net/10150/106000-
dc.descriptionArtificial Intelligence Lab, Department of MIS, Univeristy of Arizonaen_US
dc.description.abstractFear about identity verification reached new heights since the terrorist attacks on Sept. 11, 2001, with national security issues related to detecting identity deception attracting more interest than ever before. Identity deception is an intentional falsification of identity in order to deter investigations. Conventional investigation methods run into difficulty when dealing with criminals who use deceptive or fraudulent identities, as the FBI discovered when trying to determine the true identities of 19 hijackers involved in the attacks. Besides its use in post-event investigation, the ability to validate identity can also be used as a tool to prevent future tragedies. Here, we focus on uncovering patterns of criminal identity deception based on actual criminal records and suggest an algorithmic approach to revealing deceptive identities.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherACMen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectData Miningen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial intelligence laben_US
dc.subject.otherAI laben_US
dc.subject.otherCriminal profile analysisen_US
dc.titleAutomaticially Detecting Deceptive Criminal Identitiesen_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalCommunications of the ACMen_US
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