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Mining for insider threats in business transactions and processes

Protecting and securing sensitive information are critical challenges for businesses. Deliberate and intended actions such as malicious exploitation, theft or destruction of data, are not only harmful and difficult to detect, but frequently these threats are propagated by an insider. Unfortunately, current efforts to identify unauthorized access to information such as what is found in document control and management systems are limited in scope and capabilities. This paper presents an approach to detecting anomalies in business transactions and processes using a graph representation. In our graph-based anomaly detection (GBAD) approach, anomalous instances of structural patterns are discovered in data that represent entities, relationships and actions. A definition of graph-based anomalies and a brief description of the GBAD algorithms are presented, followed by empirical results using a discrete-event simulation of real-world business transactions and processes.