A damage prediction based transaction management model for database insider threat mitigation

Insider threat represents one of the most difficult the threats that an organization must consider when mitigating potential damages. In this paper, we propose a model focused on the detection of malicious insider activities and prediction of potential damage in the context of database systems. The model uses data dependency and transaction activity profiles to predict future transaction behaviors and risks associated with them. Through simulation we show the effectiveness of the model. The results prove that this model allows more transactions to finish while reducing the risk of damage to the database.