Collective framework for fraud detection using behavioral biometrics

Abstract

Fraud detection and prevention is a big data challenge, especially as business moves online. Patterns of internal or external fraud often lie in the massive amounts of unstructured machine data and log files generated by business applications and systems. Traditional fraud detection techniques rely on data collected from the user’s machine to ensure that transactions are originating from a trusted source. Such data can be altered and spoofed to fool the server and execute unauthorized transactions. Behavioral biometrics such as touch, keystroke, and mouse dynamics use measurements based on human action. Such measurements could be used to enrich the device/context signature used for fraud detection. In this chapter, we propose a new framework for fraud detection based on behavioral biometric factors. The proposed model works on both of the security and performance factors. It increases the security of existing models by targeting man-in-the-middle and replay attacks and enhances the performance by incorporating mode data in the decision-making analysis.