Think! Evidence

Modeling Human Behavior to Anticipate Insider Attacks

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dc.creator Ryan E Hohimer
dc.creator Frank L Greitzer
dc.date 2011-01-01T00:00:00Z
dc.identifier 1944-0464
dc.identifier 1944-0472
dc.identifier https://doaj.org/article/03375319cc9c4287ad16c1f744e9fa4b
dc.description The insider threat ranks among the most pressing cyber-security challenges that threaten government and industry information infrastructures. To date, no systematic methods have been developed that provide a complete and effective approach to prevent data leakage, espionage, and sabotage. Current practice is forensic in nature, relegating to the analyst the bulk of the responsibility to monitor, analyze, and correlate an overwhelming amount of data. We describe a predictive modeling framework that integrates a diverse set of data sources from the cyber domain, as well as inferred psychological/motivational factors that may underlie malicious insider exploits. This comprehensive threat assessment approach provides automated support for the detection of high-risk behavioral "triggers" to help focus the analyst's attention and inform the analysis. Designed to be domain-independent, the system may be applied to many different threat and warning analysis/sense-making problems.
dc.language English
dc.publisher Henley-Putnam University
dc.relation http://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=1094&context=jss
dc.relation https://doaj.org/toc/1944-0464
dc.relation https://doaj.org/toc/1944-0472
dc.source Journal of Strategic Security, Vol 4, Iss 2, Pp 25-48 (2011)
dc.subject Corporate security
dc.subject Cybersecurity
dc.subject Intelligence analysis
dc.subject Security management
dc.subject Terrorism / counterterrorism
dc.subject Threat assessment
dc.subject Military Science
dc.subject U
dc.subject DOAJ:Military Science
dc.subject DOAJ:Technology and Engineering
dc.title Modeling Human Behavior to Anticipate Insider Attacks
dc.type article


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