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Dark Web pp 341-353 | Cite as

Weapons of Mass Destruction (WMD) on Dark Web

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

Abstract

The tragic events of September 11 have 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. In the area under defending against catastrophic terrorism, weapons of mass destruction (WMD), especially nuclear weapons, have been considered one of the most dangerous threats to US homeland security and international peace and prosperity. We believe the science of Intelligence and Security Informatics (ISI) can help with nuclear forensics and attribution. ISI research can help advance the intelligence collection, analytical techniques, and instrumentation used in determining the origin, capability, intent, and transit route of nuclear materials by selected hostile countries and (terrorist) groups. We propose a research framework that aims to investigate the capability, accessibility, and intent of critical high-risk countries, institutions, researchers, and extremist or terrorist groups. We propose to develop a knowledge base of the Nuclear Web that will collect, analyze, and pinpoint significant actors in the high-risk international nuclear physics and weapons communities. We also identify potential extremist or terrorist groups from our Dark Web test bed who might pose WMD threats to the USA and the international community. Selected knowledge mapping and focused web crawling techniques and findings from a preliminary study are presented in this chapter.

Keywords

Terrorist Group Internet Service Provider Homeland Security Extremist Group Knowledge Mapping 
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.

Notes

Acknowledgments

Funding for this research was provided by (1) NSF, “CRI: Developing a Dark Web Collection and Infrastructure for Computational and Social Sciences,” NSF CNS-0709338, 2007–2010; and (2) NSF, “EXP-LA: Explosives and IEDs in the Dark Web: Discovery, Categorization, and Analysis,” NSF CBET-0730908, 2007–2010.

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Management Information SystemsUniversity of ArizonaTusconUSA

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