AAAI Publications, Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence

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Context-Aware Insider Threat Detection
Alex Memory, Henry G. Goldberg, Ted E. Senator

Last modified: 2013-06-28

Abstract


We are researching ways to detect insider threats in computer usage data crossing multiple modalities – e.g., resources and devices used, network and communication patterns – and where signals of possible threat are highly contextual – e.g., detectable only after inferring user roles, peer groups, collaborators and personal history. The contexts are also dynamic – reflecting a user’s rapid shifts in focus when working on different tasks and longer term changes in interests – and take place in a setting that is identity-aware but privacy-preserving. Although currently focused on the insider threat domain, the architecture, representations and algorithms we are developing are broadly applicable and can lead to interesting future research directions for context-aware computing.

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