Dark Web pp 19-30 | Cite as

Intelligence and Security Informatics (ISI): Research Framework

  • Hsinchun ChenEmail author
Part of the Integrated Series in Information Systems book series (ISIS, volume 30)


In this chapter, we review the computational research framework that is adopted by the Dark Web research. We first present the security research context, followed by description of a data mining framework for Intelligence and Security Informatics research.

The tragic events of September 11 and the following anthrax contamination of letters caused drastic effects on many aspects of society. Academics in the fields of natural sciences, computational science, information science, social sciences, engineering, medicine, and many others have been called upon to help enhance the government’s ability to fight terrorism and other crimes. Six critical mission areas have been identified where information technology can contribute, as suggested in the “National Strategy for Homeland Security” report, including: intelligence and warning, border and transportation security, domestic counterterrorism, protecting critical infrastructure, defending against catastrophic terrorism, and emergency preparedness and responses. Facing the critical missions of national security and various data and technical challenges, we believe there is a pressing need to develop the science of “Intelligence and Security Informatics” (ISI).

To address the data and technical challenges facing ISI, we present a research framework with a primary focus on KDD (Knowledge Discovery from Databases) technologies. The framework is discussed in the context of crime types and security implications. Selected data mining techniques, including information sharing and collaboration, association mining, classification and clustering, text mining, spatial and temporal mining, and criminal network analysis, are believed to be critical to criminal and intelligence analyses and investigations. In addition to the technical discussions, this chapter also discusses caveats for data mining and important civil liberties considerations.


Geographic Information System Organize Crime National Security Civil Liberty Homeland Security 
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.


  1. American Civil Liberties Union. (2004). MATRIX: Myths and Reality. Retrieved July 27, 2004, from the World Wide Web:
  2. Chen, H. (2006). Intelligence and Security Informatics for International Security: Information Sharing and Data Mining, Springer.Google Scholar
  3. Chen, H., Fuller, S. S., Friedman, C., and Hersh, W. (Eds.) (2005). Medical Informatics: Knowledge Management and Data Mining in Biomedicine. Berlin: Springer.Google Scholar
  4. Cook, J. S. and Cook, L. L. (2003). Social, ethical and legal issues of data mining. In J. Wang (Ed.), Data Mining: Opportunities and Challenges (pp. 395–420). Hershey, PA: Idea Group Publishing.CrossRefGoogle Scholar
  5. Fayyad, U. M. and Uthurusamy, R. (2002). Evolving data mining into solutions for insights. Communications of the ACM, 45(8), 28–31.CrossRefGoogle Scholar
  6. National Research Council. (2002). Making the Nation Safer: The Role of Science and Technology in Countering Terrorism. Washington, DC: National Academy Press.Google Scholar
  7. O’Harrow, R. (2005). No Place to Hide. New York: Free Press.Google Scholar
  8. Office of Homeland Security. (2002). National Strategy for Homeland Security. Washington D.C.: Office of Homeland Security.Google Scholar
  9. Sageman, M. (2004). Understanding Terror Networks. Philadelphia: University of Pennsylvania Press.CrossRefGoogle Scholar
  10. Shortliffe, E. H. and Blois, M. S. (2000). The computer meets medicine and biology: Emergence of a discipline. In K. J. Hannah and M. J. Ball (Eds.), Health Informatics (pp. 1–40). New York: Springer-Verlag.Google Scholar
  11. Strickland, L. S., Baldwin, D. A., and Justsen, M. (2005). Domestic security surveillance and civil liberties. In B. Cronin (Ed.), Annual Review of Information Science and Technology (ARIST), Volume 39. Medford, New Jersey: Information Today, Inc.Google Scholar
  12. Wang, G., Chen, H., and Atabakhsh, H. (2004a). Automatically detecting deceptive criminal ­identities. Communications of the ACM, 47(3), 71–76.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Management Information SystemsUniversity of ArizonaTusconUSA

Personalised recommendations