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

Use of Biometric Identification Technology to Reduce Fraud in the Food Stamp Program: Final Report

The report considered Arizona and eight other states' use of biometric identification technologies in food stamp programs. States surveyed in the report include California, Connecticut, Illinois, Massachusetts, New Jersey, New York, Pennsylvania and Texas. Appendix B provides profiles of the different states' programs. Includes bibliographic references

Increasing the Enlistment Bonus Cap and MOS Channeling Effects /

"Army project number 622785A790.""March 2007."Cover title.Includes bibliographical references.Contract or grant no. : DASW01-03-D-0015/DO 0025Mode of access: Internet