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Analysis of Weak Si...
Analysis of Weak Signals for Detecting Lone Wolf Terrorists
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- Brynielsson, Joel, 1974- (author)
- Swedish Defence Research Agency
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- Horndahl, Andreas, 1981- (author)
- Swedish Defence Research Agency
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- Johansson, Fredrik, 1982- (author)
- Swedish Defence Research Agency
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- Kaati, Lisa, 1975- (author)
- Swedish Defence Research Agency
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- Mårtenson, Christian, 1976- (author)
- Swedish Defence Research Agency
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- Svenson, Pontus, 1972- (author)
- Swedish Defence Research Agency
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(creator_code:org_t)
- 2012
- 2012
- English.
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In: Proceedings of the IEEE European Intelligence and Security Informatics Conference 2012 (EISIC 2012). ; , s. 197-204
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- Lone wolf terrorists pose a large threat to modern society. The current ability to identify and stop these kind of terrorists before they commit a terror act is limited since they are very hard to detect using traditional methods. However, these individuals often make use of Internet to spread their beliefs and opinions, and to obtain information and knowledge to plan an attack. Therefore, there is a good possibility that they leave digital traces in the form of weak signals that can be gathered, fused, and analyzed.In this work we present an analysis method that can be used to analyze extremist forums to profile possible lone wolf terrorists. This method is conceptually demonstrated using the FOI Impactorium fusion platform. We also present a number of different technologies that can be used to harvest and analyze information from Internet, serving as weak digital traces that can be fused using the suggested analysis method, in order to discover possible lone wolf terrorists.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- intelligence analysis
- natural language processing
- NLP
- text mining
- affect analysis
- weak signals
Publication and Content Type
- ref (subject category)
- kon (subject category)
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