Name
Abstract | ||
|---|---|---|
Analysis of dark websites is important for developing effective combating strategies against terrorism or extremists when more and more scattered terrorist cells use the ubiquity of the Internet to form communities in virtual space with fairly low costs. Terrorists or extremists anonymously set up various web sites embedded in the public Internet, exchanging ideology, spreading propaganda, and recruiting new members. In this paper, we propose a framework to discover latent topics via analyzing contents of dark websites. The content and data from dark websites are gathered and extracted by crawlers and exported to documents. Latent Dirichlet Allocation (LDA) algorithm is used to analyze the extracted documents so as to discover latent topics from web sites of terrorists or extremists. In contrast to the traditional Information Retrieval (IR) schemes, LDA-based analysis assigns a probability to a document and captures exchangeability of both words and documents. Our work helps to gain insights into the structure and communities of terrorists and extremists. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CICYBS.2009.4925106 | IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN CYBER SECURITY |
Keywords | Field | DocType |
information retrieval,latent dirichlet allocation,internet,web pages,terrorism,data mining | Latent Dirichlet allocation,World Wide Web,Web page,Computer science,Terrorism,Ideology,Virtual space,The Internet | Conference |
Citations | PageRank | References |
3 | 0.40 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Yang | 1 | 50 | 8.86 |
| Feiqiong Liu | 2 | 11 | 1.05 |
| Joseph M. Kizza | 3 | 68 | 14.06 |
| Raimund K. Ege | 4 | 197 | 93.35 |