home › event - bayesian network model for predicting insider threats


Bayesian Network Model for Predicting Insider Threats
Conferences & Talks

Workshop on Research for Insider Threat (WRIT)

24 May 2013
San Francisco, California



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.


upcoming events   view all 

The Future of the Internet: Meaning and Names or Numbers?
Glenn Edens
13 February 2015 | San Jose, CA
Conferences & Talks  

CTO Forum
Jatinder Singh
13 February 2015 | East Palo Alto, CA
Conferences & Talks  

Privacy in an Era of Big Data: Directions, Advances, and Reflections
Ersin Uzun
15 February 2015 | San Jose, CA
Conferences & Talks