Dark Web

Exploring and Data Mining the Dark Side of the Web

  • Hsinchun Chen

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

Table of contents

  1. Front Matter
    Pages i-xxvi
  2. Research Framework: Overview and Introduction

    1. Front Matter
      Pages 1-1
    2. Hsinchun Chen
      Pages 3-18
    3. Hsinchun Chen
      Pages 31-41
  3. Dark Web Research: Computational Approach and Techniques

    1. Front Matter
      Pages 43-43
    2. Hsinchun Chen
      Pages 45-69
    3. Hsinchun Chen
      Pages 71-90
    4. Hsinchun Chen
      Pages 91-103
    5. Hsinchun Chen
      Pages 105-126
    6. Hsinchun Chen
      Pages 127-151
    7. Hsinchun Chen
      Pages 153-169
    8. Hsinchun Chen
      Pages 171-201
    9. Hsinchun Chen
      Pages 203-225
    10. Hsinchun Chen
      Pages 227-256
    11. Hsinchun Chen
      Pages 257-270
  4. Dark Web Research: Case Studies

    1. Front Matter
      Pages 271-271
    2. Hsinchun Chen
      Pages 273-293
    3. Hsinchun Chen
      Pages 295-318

About this book

Introduction

The University of Arizona Artificial Intelligence Lab (AI Lab) Dark Web project is a long-term scientific research program that aims to study and understand the international terrorism (Jihadist) phenomena via a computational, data-centric approach. We aim to collect "ALL" web content generated by international terrorist groups, including web sites, forums, chat rooms, blogs, social networking sites, videos, virtual world, etc. We have developed various multilingual data mining, text mining, and web mining techniques to perform link analysis, content analysis, web metrics (technical sophistication) analysis, sentiment analysis, authorship analysis, and video analysis in our research. The approaches and methods developed in this project contribute to advancing the field of Intelligence and Security Informatics (ISI). Such advances will help related stakeholders to perform terrorism research and facilitate international security and peace.

This monograph aims to provide an overview of the Dark Web landscape, suggest a systematic, computational approach to understanding the problems, and illustrate with selected techniques, methods, and case studies developed by the University of Arizona AI Lab Dark Web team members. This work aims to provide an interdisciplinary and understandable monograph about Dark Web research along three dimensions: methodological issues in Dark Web research; database and computational techniques to support information collection and data mining; and legal, social, privacy, and data confidentiality challenges and approaches.  It will bring useful knowledge to scientists, security professionals, counterterrorism experts, and policy makers. The monograph can also serve as a reference material or textbook in graduate level courses related to information security, information policy, information assurance, information systems, terrorism, and public policy.

Keywords

Dark Web Data Mining Informatics Security Informatics Web Mining

Authors and affiliations

  • Hsinchun Chen
    • 1
  1. 1., Management Information Systems DepartmenUniversity of ArizonaTucsonUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-1557-2
  • Copyright Information Springer Science+Business Media, LLC 2012
  • Publisher Name Springer, New York, NY
  • eBook Packages Computer Science
  • Print ISBN 978-1-4614-1556-5
  • Online ISBN 978-1-4614-1557-2
  • Series Print ISSN 1571-0270
  • About this book