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Knowledge acquisition and insider threat prediction in relational database systems

  • University of Arkansas System

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

28 Scopus citations

Abstract

This paper investigates the problem of knowledge acquisition by an unauthorized insider using dependencies between objects in relational databases. It defines various types of knowledge. In addition, it introduces the Neural Dependency and Inference Graph (NDIG), which shows dependencies among objects and the amount of knowledge that can be inferred about them using dependency relationships. Moreover, it introduces an algorithm to determine the knowledgebase of an insider and explains how insiders can broaden their knowledge about various relational database objects to which they lack appropriate access privileges. In addition, it demonstrates how NDIGs and knowledge graphs help in assessment of insider threats and what security officers can do to avoid such threats.

Original languageEnglish
Title of host publicationProceedings - 12th IEEE International Conference on Computational Science and Engineering, CSE 2009 - 2009 IEEE International Conference on Privacy, Security, Risk, and Trust, PASSAT 2009
Pages450-455
Number of pages6
DOIs
StatePublished - 2009
Externally publishedYes
Event2009 IEEE International Conference on Privacy, Security, Risk, and Trust, PASSAT 2009 - Vancouver, BC, Canada
Duration: 29 Aug 200931 Aug 2009

Publication series

NameProceedings - 12th IEEE International Conference on Computational Science and Engineering, CSE 2009
Volume3

Conference

Conference2009 IEEE International Conference on Privacy, Security, Risk, and Trust, PASSAT 2009
Country/TerritoryCanada
CityVancouver, BC
Period29/08/0931/08/09

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