Modeling Expert Judgments of Insider Threat Using Ontology Structure: Effects of Individual Indicator Threat Value and Class Membership

Greitzer, Frank
Purl, Justin
Becker, D.E. (Sunny)
Sticha, Paul
Leong, Yung Mei
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We describe research on a comprehensive ontology of sociotechnical and organizational factors for insider threat (SOFIT) and results of an expert knowledge elicitation study. The study examined how alternative insider threat assessment models may reflect associations among constructs beyond the relationships defined in the hierarchical class structure. Results clearly indicate that individual indicators contribute differentially to expert judgments of insider threat risk. Further, models based on ontology class structure more accurately predict expert judgments. There is some (although weak) empirical evidence that other associations among constructs—such as the roles that indicators play in an insider threat exploit—may also contribute to expert judgments of insider threat risk. These findings contribute to ongoing research aimed at development of more effective insider threat decision support tools.
Inside the Insider Threats, Digital Government, insider threat, ontology, threat assessment
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