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Probabilistic Modeling of Insider Threat Detection Systems

  • Brian RuttenbergEmail author
  • Dave Blumstein
  • Jeff Druce
  • Michael Howard
  • Fred Reed
  • Leslie Wilfong
  • Crystal Lister
  • Steve Gaskin
  • Meaghan Foley
  • Dan Scofield
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10744)

Abstract

Due to the high consequences of poorly performing automated insider threat detection systems (ITDSs), it is advantageous for Government and commercial organizations to understand the performance and limitations of potential systems before their deployment. We propose to capture the uncertainties and dynamics of organizations deploying ITDSs to create an accurate and effective probabilistic graphical model that forecasts the operational performance of an ITDS throughout its deployment. Ultimately, we believe this modeling methodology will result in the deployment of more effective ITDSs.

Keywords

Insider threat Probabilistic relational models 

Notes

Acknowledgments

This work was supported by IARPA contract 2016-16031100002. The views expressed are those of the authors and do not reflect the official policy or position of the U.S. Government.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Brian Ruttenberg
    • 1
    Email author
  • Dave Blumstein
    • 1
  • Jeff Druce
    • 1
  • Michael Howard
    • 1
  • Fred Reed
    • 1
  • Leslie Wilfong
    • 2
  • Crystal Lister
    • 2
  • Steve Gaskin
    • 3
  • Meaghan Foley
    • 4
  • Dan Scofield
    • 4
  1. 1.Charles River AnalyticsCambridgeUSA
  2. 2.Cognitio Corp.McLeanUSA
  3. 3.Applied Marketing ScienceWalthamUSA
  4. 4.Assured Information SystemsRomeUSA

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