Home | Browse | Search | Credits | About
Register | User Area | DL-Harvest | Help
DLIST

Automaticially Detecting Deceptive Criminal Identities

Wang, Gang and Chen, Hsinchun and Atabakhsh, Homa (2004) Automaticially Detecting Deceptive Criminal Identities. Communications of the ACM 47(3):pp. 71-76.

Full text available as:
PDF - Requires Adobe Acrobat Reader or other PDF viewer.

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.

EPrint Type:Journal Article (Paginated)
Keywords:National Science Digital Library, NSDL, Artificial Intelligence Lab, AI Lab, Criminal Profile Analysis
Subjects:Artificial Intelligence
Data Mining
ID Code:434
Deposited On:17 August 2004
Alternative Locations:http://ai.bpa.arizona.edu/go/papers.html
Eprint Statistics:View statistics for this eprint
Tell A Colleague:Tell a colleague about it.

1. Burgoon, J.K., Buller, D.B., Guerrero, L.K., Afifi, W., and Feldman, C. Interpersonal deception: XII. Information management dimensions underlying deceptive and truthful messages. Communication Monographs 63 (1996). 50–69.

2. Hauck, R.V., Atabakhsh, H., Ongvasith, P., Gupta, H., and Chen, H. Using COPLINK to analyze criminal-justice data. IEEE Computer (Mar. 2002).

3. Jaro, M.A. UNIMATCH: A Record Linkage System: User’s Manual. Technical Report, U.S. Bureau of the Census, Washington, DC, 1976.

4. Levenshtein, V.L. Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady 10, (1966), 707–710.

5. Newcombe, H.B., et al. Automatic linkage of vital records. Science 130, 3381 (1959), 954–959.

6. Porter, E.H. and Winkler, W.E. Approximate string comparison and its effect on an advanced record linkage system. Record Linkage Techniques (1997), 190–202.

7. Vrij, A. Detecting Lies and Deceit: The Psychology of Lying and the Implication for Professional Practice. John Wiley, 2000.

8. Winkler, W.E.. The state of record linkage and current research problems. In Proceedings of the Section on Survey Methods of the Statistical Society of Canada,1999. (Also in technical report, RR99/04. U.S. Census Bureau; www.census.gov/srd/papers/pdf/rr99-04.pdf)

EPrints dLIST, an open access archive for the Information Sciences, is supported by the School of Information Resources and Library Science and Learning Technologies Center, University of Arizona. Established in 2002, dLIST has a global Advisory Board and is a part of the Information Technology & Society Research Lab. Open Archives
Contact: Admin | Donate