Tobias Andreas Fritz M.Sc.

Research Area:

Tobias Fritz is a researcher in machine learning with a focus on temporal graph neural networks (TGNNs). His work combines foundational research on performance and efficiency with applications in security and networking. He develops and evaluates TGNNs for blockchain forensics (cryptocurrency transaction graphs; fraud and ransomware activity), large-scale social networks (e.g., Twitter/X), and network-traffic analytics (e.g. in 5G core networks).

Publications:

Tobias Fritz, Alexander Schwankner, Jan-Hendrik Wissing, Robin Buchta, Gabi Dreo Rodosek. May 2025. Granomaly: A Framework for Anomaly Detection in 5G Core Network Control Plane Traffic with Temporal Graph Neural Networks. Network Operations and Management Symposium (NOMS) 2025.

Robin Buchta, Tobias Fritz, Carsten Kleiner, Felix Heine, Gabi Dreo Rodosek. May 2024. One-Class Learning on Temporal Graphs for Attack Detection in Cyber-Physical Systems. Network Operations and Management Symposium, Workshop on Analytics for Network and Service Management (NOMS AnNet) 2024.

Tobias Fritz. September 2023. Leveraging tree-structured Graphs in Graph Neural Networks for Fake News Detection.  PhD Symposium Poster @ European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML  PKDD) 2023

L. Servadei, E. Zennaro, T. Fritz, K. Devarajegowda, W. Ecker, R. Wille. November 2019.  Using Machine Learning for Predicting Area and Firmware Metrics of Hardware Designs from Abstract Specifications. In Microprocessors and Microsystems, Volume 71, November 2019