Experimental Investigation of Demographic Factors Related to Phishing Susceptibility

This paper reports on a simulated phishing experiment targeting 6,938 faculty and staff at George Mason University. The study examined various possible predictors of phishing susceptibility. The focus of the present paper is on demographic factors (including age, gender and position/employment). Since previous studies of age and gender have yielded discrepant results, one purpose of the study was to disambiguate these findings. A second purpose was to compare different types of email phishing exploits. A third objective was to compare the effect of different types of feedback given to those who clicked on one or more of three simulated phishing exploits that were deployed over a three-week period. Our analysis of demographic factors, effects of phishing email content, and effects of repeated exposure to phishing exploits revealed significant age effects, marginally significant gender differences, and significant differences in email type. A multi-level model estimated effects of multiple variables simultaneously

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

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