Automatically detecting deceptive criminal identities

Gang Wang; Hsinchun Chen; Homa Atabakhsh

doi:10.1145/971617.971618

Automatically detecting deceptive criminal identities

Gang Wang, Hsinchun Chen, Homa Atabakhsh

Research output: Contribution to journal › Review article › peer-review

80 Scopus citations

Abstract

The uncovering patterns of criminal identity deception based on actual criminal records and algorithmic approach to reveal deceptive identities are discussed. The testing results shows that no false positive errors occurs which shows the effectiveness of the algorithm. The errors occurs in the false negative category in which unrelated suspects are recognized as being related. The threshold value is set to capture maximum possible true similar records. Adaptive threshold is required for making an automated process in the future research.

Original language	English (US)
Pages (from-to)	70-76
Number of pages	7
Journal	Communications of the ACM
Volume	47
Issue number	3
DOIs	https://doi.org/10.1145/971617.971618
State	Published - Mar 2004

ASJC Scopus subject areas

General Computer Science

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10.1145/971617.971618

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title = "Automatically detecting deceptive criminal identities",

abstract = "The uncovering patterns of criminal identity deception based on actual criminal records and algorithmic approach to reveal deceptive identities are discussed. The testing results shows that no false positive errors occurs which shows the effectiveness of the algorithm. The errors occurs in the false negative category in which unrelated suspects are recognized as being related. The threshold value is set to capture maximum possible true similar records. Adaptive threshold is required for making an automated process in the future research.",

author = "Gang Wang and Hsinchun Chen and Homa Atabakhsh",

year = "2004",

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AU - Atabakhsh, Homa

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N2 - The uncovering patterns of criminal identity deception based on actual criminal records and algorithmic approach to reveal deceptive identities are discussed. The testing results shows that no false positive errors occurs which shows the effectiveness of the algorithm. The errors occurs in the false negative category in which unrelated suspects are recognized as being related. The threshold value is set to capture maximum possible true similar records. Adaptive threshold is required for making an automated process in the future research.

AB - The uncovering patterns of criminal identity deception based on actual criminal records and algorithmic approach to reveal deceptive identities are discussed. The testing results shows that no false positive errors occurs which shows the effectiveness of the algorithm. The errors occurs in the false negative category in which unrelated suspects are recognized as being related. The threshold value is set to capture maximum possible true similar records. Adaptive threshold is required for making an automated process in the future research.

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DO - 10.1145/971617.971618

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Automatically detecting deceptive criminal identities

Abstract

ASJC Scopus subject areas

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