ANUBIS: Aligned oNline and multilevel User and entity Behavior analytics for Intelligent System security

  • Verbeke, Wouter, (Administrative Promotor)

Project Details

Description

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 organization- specific. 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
Short titleANUBIS
AcronymFWOAL957
StatusActive
Effective start/end date1/01/2031/12/23

Flemish discipline codes

  • Data collection and data estimation methodology, computer programs
  • Econometric and statistical methods and methodology
  • Mathematical and quantitative methods not elsewhere classified
  • Mathematical methods, programming models, mathematical and simulation modelling

Keywords

  • Business Analytics
  • Fraud detection
  • robust statistics