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.
CITATION STYLE
Ruttenberg, B., Blumstein, D., Druce, J., Howard, M., Reed, F., Wilfong, L., … Scofield, D. (2018). Probabilistic modeling of insider threat detection systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10744 LNCS, pp. 91–98). Springer Verlag. https://doi.org/10.1007/978-3-319-74860-3_6
Mendeley helps you to discover research relevant for your work.