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.
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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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Ruttenberg, B. et al. (2018). Probabilistic Modeling of Insider Threat Detection Systems. In: Liu, P., Mauw, S., Stolen, K. (eds) Graphical Models for Security. GraMSec 2017. Lecture Notes in Computer Science(), vol 10744. Springer, Cham. https://doi.org/10.1007/978-3-319-74860-3_6
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DOI: https://doi.org/10.1007/978-3-319-74860-3_6
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