Machine Learning for Cyber Security & Fraud Detection

In the field of cyber security and fraud prevention, our research at the Chair focuses on developing machine learning-based approaches for anomaly detection.  Our primary focus lies in developing and utilizing machine learning algorithms to detect anomalies and suspicious activities in network security, host-based security and fraud detection in enterprise resource planning (ERP) systems. We aim to identify potential vulnerabilities and threats within these systems, enabling proactive measures to ensure data and system integrity.

• DeepScan -  Machine Learning for automatic detection of security relevant events and fraud.