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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Li Yang Feiqiong Liu Kizza, J.M. Ege, R.K. |
| Copyright Year | 2009 |
| Description | Author affiliation: Department of Computer Science and Engineering, University of Tennessee at Chattanooga, USA (Feiqiong Liu; Kizza, J.M.) || Department of Computer Science, Northern Illinois University, USA (Ege, R.K.) |
| 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. |
| Starting Page | 175 |
| Ending Page | 179 |
| File Size | 2135875 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424427697 |
| DOI | 10.1109/CICYBS.2009.4925106 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-03-30 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Algorithm design and analysis Costs Terrorism Crawlers Scattering Information retrieval Internet Linear discriminant analysis Data mining Recruitment |
| Content Type | Text |
| Resource Type | Article |
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