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Suspect Vehicle Identification for Border Safety

  • Siddharth Kaza
  • Hsinchun Chen
Part of the Studies in Computational Intelligence book series (SCI, volume 135)

Abstract

Border safety is a critical part of national and international security. The U.S. Department of Homeland Security searches vehicles entering the country at land borders 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 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. In a partnership with border-area law enforcement agencies and CBP, we include these heuristics in the MI formulation and identify suspect vehicles using large-scale, real-world data collections. Statistical tests and selected cases judged by domain experts show that the heuristic-enhanced MI performs significantly better than classical MI in identifying pairs of potentially criminal vehicles. The techniques described can be used to assist CBP agents perform their functions both efficiently and effectively.

Keywords

Mutual Information Association Rule Association Rule Mining Homeland Security Police Contact 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Siddharth Kaza
    • 1
  • Hsinchun Chen
    • 1
  1. 1.Department of Management Information SystemsUniversity of ArizonaUSA

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