Enhancing border security: Mutual information analysis to identify suspect vehicles

Siddharth Kaza, Yuan Wang, Hsinchun Chen

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

In recent years border safety has been identified as a critical part of homeland security. The Department of Homeland Security searches vehicles entering the country for drugs and other contraband. Customs and Border Protection (CBP) agents believe that such vehicles operate in groups and if the criminal links of one vehicle are known then their border crossing patterns can be used to identify other partner vehicles. We perform this association analysis by using mutual information (MI) to identify pairs of vehicles that may be involved in criminal activity. CBP agents also suggest that criminal vehicles may cross at certain times or ports to try and evade inspection. We propose to modify the MI formulation to include these heuristics by using law enforcement data from border-area jurisdictions. Statistical tests and selected cases judged by domain experts show that modified MI performs significantly better than classical MI in identifying potentially criminal vehicles.

Original languageEnglish (US)
Pages (from-to)199-210
Number of pages12
JournalDecision Support Systems
Volume43
Issue number1
DOIs
StatePublished - Feb 2007

Keywords

  • Border safety
  • Intelligence and security informatics
  • Mutual information

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management

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