Biblio

Recently, malicious insider attacks represent one of the most damaging threats to companies and government agencies. This paper proposes a new framework in constructing a user-centered machine learning based insider threat detection system on multiple data granularity levels. System evaluations and analysis are performed not only on individual data instances but also on normal and malicious insiders, where insider scenario specific results and delay in detection are reported and discussed. Our results show that the machine learning based detection system can learn from limited ground truth and detect new malicious insiders with a high accuracy.

The survey of related works on insider information security (IS) threats is presented. Special attention is paid to works that consider the insiders' behavioral models as it is very up-to-date for behavioral intrusion detection. Three key research directions are defined: 1) the problem analysis in general, including the development of taxonomy for insiders, attacks and countermeasures; 2) study of a specific IS threat with forecasting model development; 3) early detection of a potential insider. The models for the second and third directions are analyzed in detail. Among the second group the works on three IS threats are examined, namely insider espionage, cyber sabotage and unintentional internal IS violation. Discussion and a few directions for the future research conclude the paper.