Probabilistic Modeling of Insider Threat Detection Systems

Ruttenberg, B.1, Blumstein, D.1, Druce, J.1, Howard, M.1, Reed, F.1, Wilfong, L.2, Lister, C.2, Gaskin, S.3, Foley, M.4, and Scofield, D.4

Presented at The Fourth International Workshop on Graphical Models for Security (GraMSec 2017), Santa Barbara, CA (August 2017)

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.

1 Charles River Analytics
2 Cognitio Corp.
3 Applied Marketing Science
4 Assured Information Systems

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