Knowledge Acquisition and Insider Threat Prediction in Relational Database Systems

This paper investigates the problem of knowledge acquisition by an unauthorized insider using dependencies between objects in relational databases. It defines various types of knowledge. In addition, it introduces the Neural Dependency and Inference Graph (NDIG), which shows dependencies among objects and the amount of knowledge that can be inferred about them using dependency relationships. Moreover, it introduces an algorithm to determine the knowledgebase of an insider and explains how insiders can broaden their knowledge about various relational database objects to which they lack appropriate access privileges. In addition, it demonstrates how NDIGs and knowledge graphs help in assessment of insider threats and what security officers can do to avoid such threats.