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Intern
Lehrstuhl für Informatik III

Katharina Dietz M. Sc.

Telefon (0931) 31-87343
E-Mail katharina.dietz@informatik.uni-wuerzburg.de
Raum A212

Anschrift

Lehrstuhl für Informatik III
Am Hubland
D-97074 Würzburg

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.

2022[ to top ]
  • Dietz, K., Gray, N., Seufert, M., & Hoßfeld, T. 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. Browser Fingerprinting: How to Protect Machine Learning Models and Data with Differential Privacy?. 1st International Workshop on Machine Learning in Networking (MaLeNe).
  • Gray, N., Dietz, K., Seufert, M., & Hoßfeld, T. 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. Identification and Evaluation of KPIs in SDN via Simulation for Establishing Topology Classifications and Prediction Models [Master thesis]. University of Würzburg.
  • Gray, N., Dietz, K., & Hoßfeld, T. 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. Extending the OpenFlow OMNeT++ Suite [Master thesis]. University of Würzburg.