ANUBIS: Aligned oNline and multilevel User and entity Behavior (G015020N)

Fraud is a fierce threat to digital business. A typical organization is estimated to lose 5% of its revenues due to fraud, which is hard to eradicate since dynamic, system-dependent and organizationspecific. Powerful and intelligent fraud detection systems are therefore of crucial importance, to timely block, prevent and contain fraud and to mitigate losses. User and entity behavior analytics essentially profile the activity of users, peer groups and other entities such as devices, applications and networks, with the aim to detect anomalous patterns which are indicative for security threats, such as fraud.

In this research project, we will improve the adaptiveness and detection power of user and entity behavior analytics by aligning the objective of these approaches when learning from data with the business objective of minimizing fraud losses, instead of maximizing performance from a statistical perspective. For this purpose, we will leverage and advance upon profit driven analytics and cost-sensitive ensemble learning approaches. Additionally, we will extend these approaches to accommodate online and multilevel learning from streaming data from across systems and applications. The developed approaches will be empirically evaluated on available data sets and benchmarked to state-of-the-art approaches.