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Deutsch Intern
Chair of Computer Science III

Katharina Dietz M. Sc.

Telefon (0931) 31-87343 (im Moment nicht besetzt, bitte per Mail melden)
E-Mail katharina.dietz@informatik.uni-wuerzburg.de
Raum A212

Anschrift

Lehrstuhl für Informatik III
Am Hubland
D-97074 Würzburg

Contents:  Overview | Working Group | Projects | Teaching | Publications

Research Interests

Some of my research interests include the following:

  • Everything related to network management, e.g., performance prediction or anomaly detection
  • Machine Learning (ML) in communication networks, including but not limited to
    • Data synthetization via Generative Adversarial Networks (GANs)
    • Transfer Learning mechanisms
    • Active Learning mechanisms
    • Uncertainty in ML
  • Network simulation with OMNeT++ and other tools
  • Statistics evaluation and ML in R and Python

Open theses can be found here. Note that a lot of these topics can be tailored to either bachelor theses, master theses or student projects.

2023[ to top ]
  • Dietz, K., Wehner, N., Casas, P., Hoßfeld, T., & Seufert, M. (2023, September). Demystifying User-based Active Learning for Network Monitoring Tasks. 2nd Workshop on Machine Learning & Netwoking (MaLeNe) As Part of NetSys’23.
  • Dietz, K., Seufert, M., & Hoßfeld, T. (2023). Want more WANs? Comparison of Traditional and GAN-based Generation of Wide Area Network Topologies via Graph and Performance Metrics. Transactions on Network and Service Management.
  • Dietz, K., Gray, N., Wolz, M., Lorenz, C., Hoßfeld, T., & Seufert, M. (2023, May). Moving Down the Stack: Performance Evaluation of Packet Processing Technologies for Stateful Firewalls. 34th IEEE IFIP Network Operations and Management Symposium (NOMS).
2022[ to top ]
  • Dietz, K., Seufert, M., & Hoßfeld, T. (2022, October). Comparing Traditional and GAN-based Approaches for the Synthesis of Wide Area Network Topologies. 18th International Conference on Network and Service Management (CNSM).
  • Dietz, K., Gray, N., Seufert, M., & Hoßfeld, T. (2022, April). ML-based Performance Prediction of SDN using Simulated Data from Real and Synthetic Networks. 33th IEEE IFIP Network Operations and Management Symposium (NOMS).
2021[ to top ]
  • Dietz, K., Mühlhauser, M., Seufert, M., Gray, N., Hoßfeld, T., & Herrmann, D. (2021, September). Browser Fingerprinting: How to Protect Machine Learning Models and Data with Differential Privacy?. 1st International Workshop on Machine Learning in Networking (MaLeNe) As Part of NetSys’21.
  • Gray, N., Dietz, K., Seufert, M., & Hoßfeld, T. (2021, June). High Performance Network Metadata Extraction Using P4 for ML-Based Intrusion Detection Systems. 22nd International Conference on High Performance Switching and Routing (HPSR).
2020[ to top ]
  • Dietz, K. (2020). Identification and Evaluation of KPIs in SDN via Simulation for Establishing Topology Classifications and Prediction Models (1–) [Master thesis]. University of Würzburg.
  • Gray, N., Dietz, K., & Hoßfeld, T. (2020). Simulative Evaluation of KPIs in SDN for Topology Classification and Performance Prediction Models. 2020 16th International Conference on Network and Service Management (CNSM).
2019[ to top ]
  • Dietz, K. (2019). Extending the OpenFlow OMNeT++ Suite (1–) [Master thesis]. University of Würzburg.