Katharina Dietz M. Sc.
Telefon | (0931) 31-87343 |
katharina.dietz@informatik.uni-wuerzburg.de | |
Raum | A212 |
Anschrift | Lehrstuhl für Informatik III |
Research Interests
Some of my research interests include the following:
- Everything related to network management, e.g., performance prediction or anomaly detection
- Artificial Intelligence (AI) and Machine Learning (ML) in communication networks, including but not limited to
- Data synthetization via Generative Adversarial Networks (GANs)
- Usability and Explainability of AI/ML
- Active Learning mechanisms
- Uncertainty in AI/ML
- Network simulation with OMNeT++ and other tools
- Statistics evaluation and AI/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.
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The Missing Link in Network Intrusion Detection: Taking AI/ML Research Efforts to Users. IEEE Access. (2024).
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HALIDS: a hardware-assisted machine learning IDS for in-network monitoring. In: 2024 8th Network Traffic Measurement and Analysis Conference (TMA), Dresden, Germany (2024).
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Marina: Realizing ML-Driven Real-Time Network Traffic Monitoring at Terabit Scale. Transactions on Network and Service Management (TNSM). (2024).
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Demystifying User-based Active Learning for Network Monitoring Tasks. In: 2nd Workshop on Machine Learning & Networking (MaLeNe) as part of NetSys’23. , Potsdam, Germany (2023).
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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 (TNSM). (2023).
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Moving Down the Stack: Performance Evaluation of Packet Processing Technologies for Stateful Firewalls. In: 34th IEEE/IFIP Network Operations and Management Symposium (NOMS). , Miami, Florida (2023).
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Comparing Traditional and GAN-based Approaches for the Synthesis of Wide Area Network Topologies. In: 18th International Conference on Network and Service Management (CNSM). , Thessaloniki, Greece (2022).
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ML-based Performance Prediction of SDN using Simulated Data from Real and Synthetic Networks. In: 33th IEEE/IFIP Network Operations and Management Symposium (NOMS). , Budapest, Hungary (2022).
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Browser Fingerprinting: How to Protect Machine Learning Models and Data with Differential Privacy?. In: 1st International Workshop on Machine Learning in Networking (MaLeNe) as part of NetSys’21. , Lübeck, Germany (Virtual Conference) (2021).
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High Performance Network Metadata Extraction Using P4 for ML-Based Intrusion Detection Systems. In: 22nd International Conference on High Performance Switching and Routing (HPSR). , Paris, France (Virtual Conference) (2021).
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Identification and Evaluation of KPIs in SDN via Simulation for Establishing Topology Classifications and Prediction Models, (2020).
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Simulative Evaluation of KPIs in SDN for Topology Classification and Performance Prediction Models. In: 2020 16th International Conference on Network and Service Management (CNSM) (2020).
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Extending the OpenFlow OMNeT++ Suite, (2019).