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Anomalous user behavior detection is the core component of many informationsecurity systems, such as intrusion detection, insider threat detection andauthentication systems. Anomalous behavior will raise an alarm to the systemadministrator and can be further combined with other information to determinewhether it constitutes an unauthorised or malicious use of a resource. Thispaper presents an anomalous user behaviour detection framework that applies anextended version of Isolation Forest algorithm. Our method is fast and scalableand does not require example anomalies in the training data set. We apply ourmethod to an enterprise dataset. The experimental results show that the systemis able to isolate anomalous instances from the baseline user model using asingle feature or combined features.

Detecting Anomalous User Behavior Using an Extended Isolation Forest Algorithm: An Enterprise Case Study