Detecting insider information theft using features from file access logs

Abstract

Access control is a necessary, but often insufficient, mechanism for protecting sensitive resources. In some scenarios, the cost of anticipating information needs and specifying precise access control policies is prohibitive. For this reason, many organizations provide employees with excessive access to some resources, such as file or source code repositories. This allows the organization to maximize the benefit employees get from access to troves of information, but exposes the organization to excessive risk. In this work we investigate how to build profiles of normal user activity on file repositories for uses in anomaly detection, insider threats, and risk mitigation. We illustrate how information derived from other users' activity and the structure of the filesystem hierarchy can be used to detect abnormal access patterns. We evaluate our methods on real access logs from a commercial source code repository on tasks of user identification and users seeking to leak resources by accessing more than they have a need for. © 2014 Springer International Publishing Switzerland.