Dark Web: Exploring and Data Mining the Dark Side of the Web

Dark Web: Exploring and Data Mining the Dark Side of the Web

Ramesh Sharda

Language: English

Pages: 459

ISBN: 2:00255698

Format: PDF / Kindle (mobi) / ePub

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). S

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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. 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. • Chapter 3. Terrorism Informatics In

ideological goal, and deliberately target or disregard the safety of noncombatants (civilians) (http://en.wikipedia.org/wiki/Terrorism). Rooted in political science, terrorism study has attracted researchers from many social science disciplines, from international relations to communications, and from defense analysis H. Chen, Dark Web: Exploring and Data Mining the Dark Side of the Web, Integrated Series in Information Systems 30, DOI 10.1007/978-1-4614-1557-2_3, © Springer Science+Business

forum–focused crawler faces several design challenges. One major concern is accessibility. Web forums are dynamic and often require memberships. They are part of the “hidden web” (Florescu et al. 1998; Raghavan and Garcia-Molina 2001) which is not easily accessible through normal web navigation or standard crawling. There are also multilingual web H. Chen, Dark Web: Exploring and Data Mining the Dark Side of the Web, Integrated Series in Information Systems 30, DOI 10.1007/978-1-4614-1557-2_4, ©

explained above. Each of these six dimensions represents a normalized scale between 0 and 1 86 5 Link and Content Analysis Cluster 1: Hizballah Cluster Cluster 2: Palestinian Cluster Virtual community Cluster 3: Al-Qaeda Cluster Communications Communications Fundraising 0.53 Communications Virtual community Virtual community Fundraising Fundraising 0.52 0.43 0.35 0.20 0.13 0.32 0.12 0.10 0.30 0.44 0.50 0.81 0.30 0.92 Sharing ideology Propaganda (outsiders) Sharing

existing nodes. At each time step, a random pair of existing nodes may get connected depending on the number of common neighbors they have. The more common neighbors they share the more likely it is that they will be connected. Mechanisms (a) and (b) were expected to generate the power-law degree distribution, and mechanism (c) was expected to generate the high clustering coefficient and two-regime scaling behavior. Through the simulations, we found that the powerlaw degree distributions could be

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