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A Bayesian Network Model for Predicting Insider Threats

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4 Author(s)
Elise T. Axelrad ; Human Resources Res. Organ. (HumRRO), Alexandria, VA, USA ; Paul J. Sticha ; Oliver Brdiczka ; Jianqiang Shen

This paper introduces a Bayesian network model for the motivation and psychology of the malicious insider. First, an initial model was developed based on results in the research literature, highlighting critical variables for the prediction of degree of interest in a potentially malicious insider. Second, a survey was conducted to measure these predictive variables in a common sample of normal participants. Third, a structural equation model was constructed based on the original model, updated based on a split-half sample of the empirical survey data and validated against the other half of the dataset. Fourth, the Bayesian network was adjusted in light of the results of the empirical analysis. Fifth, the updated model was used to develop an upper bound on the quality of model predictions of its own simulated data. When empirical data regarding psychological predictors were input to the model, predictions of counterproductive behavior approached the upper bound of model predictiveness.

Published in:

Security and Privacy Workshops (SPW), 2013 IEEE

Date of Conference:

23-24 May 2013