Individuals, organizations, and devices are now interconnected to an unprecedented degree. This has forced identity risk analysts to redefine what “identity” means in such a context, and to explore new techniques for analyzing an ever expanding threat context. Major hurdles to modeling in this field include the inherent lack of publicly available data due to privacy and safety concerns, as well as the unstructured nature of incident reports. To address this, this report develops a system for strengthening an identity risk model using the text mining of news stories. The system—called NewsFerret—collects and analyzes news stories on the topic of identity theft, establishes semantic relatedness measures between identity concept pairs, and supports analysis of those measures through reports, visualizations, and relevant news stories. Evaluating the resulting analytical models shows where the system is effective in assisting the risk analyst to expand and validate identity risk models. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/22743 |
| Date | 18 December 2013 |
| Creators | Golden, Ryan Christian |
| Source Sets | University of Texas |
| Language | en_US |
| Detected Language | English |
| Format | application/pdf |
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