Suspect Vehicle Identification for Border Safety with Modified Mutual Information

  • Siddharth Kaza
  • Yuan Wang
  • Hsinchun Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3975)


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.


Mutual Information Homeland Security License Plate Border Crossing 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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  1. 1.
    National Strategy for Homeland Security. Office of Homeland Security (2002)Google Scholar
  2. 2.
    Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in large Databases. In: Proc. of ACM SIGMOD Conference on Management of Data (1993)Google Scholar
  3. 3.
    Berrar, D., Dubitzky, W., Granzow, M., Eils, R.: Analysis of Gene Expression and Drug Activity Data by Knowledge-Based Association Mining. In: Proc. of Critical Assessment of Microarray Data Analysis Techniques, CAMDA 2001 (2001)Google Scholar
  4. 4.
    Chen, I.-M.A., Rotem, D.: Integrating Information from Multiple Independently Developed Data Sources. In: Proc. of 7th International Conference on Information and Knowledge Management, Bethesda, Maryland (1998)Google Scholar
  5. 5.
    Chruch, K.W., Hanks, P.: Word Association Norms, Mutual Information, and Lexicography. Computational Linguistics 16, 22–29 (1990)Google Scholar
  6. 6.
    Fano, R.M.: Transmission of Information. MIT Press, Cambridge (1961)Google Scholar
  7. 7.
    Kaza, S., Wang, T., Gowda, H., Chen, H.: Target Vehicle Identification for Border Safety using Mutual Information. In: Proc. of 8th International IEEE Conference on Intelligent Transportation Systems, Vienna, Austria (2005)Google Scholar
  8. 8.
    Lee, W., Stolfo, S.J.: Data Mining Approaches for Intrusion Detection. In: Proc. of 7th USENIX Security Symposium (1998)Google Scholar
  9. 9.
    Magerman, D.M., Marcus, M.P.: Parsing a Natural Language using Mutual Information Statistics. In: Proc. of Eight National Conference on Artificial Intelligence (1990)Google Scholar
  10. 10.
    Marshall, B., Kaza, S., Xu, J., Atabakhsh, H., Petersen, T., Violette, C., Chen, H.: Cross-Jurisdictional Criminal Activity Networks to Support Border and Transportation Security. In: Proc. of 7th International IEEE Conference on Intelligent Transportation Systems, Washington D.C (2004)Google Scholar
  11. 11.
    Mobasher, B., Jain, N., Han, E.H., Srivastava, J.: Web mining: Pattern discovery from world wide web transactions. Department of Computer Science, University of Minnesota. Minneapolis. Technical Report (1996)Google Scholar
  12. 12.
    Ong, T., Chen, H.: Updateable PAT-Tree Approach to Chinese Key Phrase Extraction Using Mutual Information: a Linguistic Foundation for Knowledge Management. In: Proc. of Second Asian Digital Library Conference, Taipei, Taiwan (1999)Google Scholar
  13. 13.
    Pantel, P., Philpot, A., Hovy, E.: Aligning Database Columns using Mutual Information. In: Proc. of The 6th National Conference on Digital Government Research (dg.o), Atlanta, GA (2005)Google Scholar
  14. 14.
    Stonebraker, M., Agrawal, R., Dayal, U., Neuhold, E., Reuter, A.: The DBMS Research at Crossroads. In: Proc. of The VLDB Conference, Dublin (1993)Google Scholar
  15. 15.
    Tao, T., Zhai, C.X., Lu, X., Fang, H.: A study of statistical methods for function prediction of protein motifs. Applied Bioinformatics 3, 115–124 (2004)CrossRefGoogle Scholar
  16. 16.
    Wren, J.D.: Extending the Mutual Information Measure to Rank Inferred Literature Relationships. BMC Bioinformatics 5 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Siddharth Kaza
    • 1
  • Yuan Wang
    • 1
  • Hsinchun Chen
    • 1
  1. 1.Department of Management Information SystemsUniversity of Arizona 

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