Suspect vehicle identification for border safety with modified mutual information

Authors:
Siddharth Kaza;Yuan Wang;Hsinchun Chen
Affiliations:
Department of Management Information Systems, University of Arizona;Department of Management Information Systems, University of Arizona;Department of Management Information Systems, University of Arizona
Venue:
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Year:
2006

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Abstract

The Department of Homeland Security monitors vehicles entering and leaving the country at land ports of entry. Some vehicles are targeted to search for drugs and other contraband. Customs and Border Protection agents believe that vehicles involved in illegal activity operate in groups. 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 are potentially involved in criminal activity. Domain experts also suggest that criminal vehicles may cross at certain times of the day to evade inspection. We propose to modify the mutual information formulation to include this heuristic by using cross-jurisdictional criminal data from border-area jurisdictions. We find that the modified MI with time heuristics performs better than classical MI in identifying potentially criminal vehicles.