Katharina Dietz M. Sc.

2021

1.
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). , Lübeck, Germany (Virtual Conference) (2021).
[ BibTeX ] [ EndNote ]
 
@inproceedings{info3-inproceedings-2021-16, address = {Lübeck, Germany (Virtual Conference)}, author = {Dietz, Katharina and Mühlhauser, Michael and Seufert, Michael and Gray, Nicholas and Hoßfeld, Tobias and Herrmann, Dominik}, booktitle = {1st International Workshop on Machine Learning in Networking (MaLeNe)}, keywords = {myown wintermute sys:hidden:inproceedings ucn}, month = 9, title = {Browser Fingerprinting: How to Protect Machine Learning Models and Data with Differential Privacy?}, year = 2021 }
%0 Conference Paper %1 info3-inproceedings-2021-16 %A Dietz, Katharina %A Mühlhauser, Michael %A Seufert, Michael %A Gray, Nicholas %A Hoßfeld, Tobias %A Herrmann, Dominik %B 1st International Workshop on Machine Learning in Networking (MaLeNe) %C Lübeck, Germany (Virtual Conference) %D 2021 %T Browser Fingerprinting: How to Protect Machine Learning Models and Data with Differential Privacy?
2.
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). , Paris, France (Virtual Conference) (2021).
[ BibTeX ] [ EndNote ]
 
@inproceedings{info3-inproceedings-2021-8, address = {Paris, France (Virtual Conference)}, author = {Gray, Nicholas and Dietz, Katharina and Seufert, Michael and Hoßfeld, Tobias}, booktitle = {22nd International Conference on High Performance Switching and Routing (HPSR)}, keywords = {myown wintermute sys:hidden:inproceedings ucn}, month = 6, title = {High Performance Network Metadata Extraction Using P4 for ML-Based Intrusion Detection Systems}, year = 2021 }
%0 Conference Paper %1 info3-inproceedings-2021-8 %A Gray, Nicholas %A Dietz, Katharina %A Seufert, Michael %A Hoßfeld, Tobias %B 22nd International Conference on High Performance Switching and Routing (HPSR) %C Paris, France (Virtual Conference) %D 2021 %T High Performance Network Metadata Extraction Using P4 for ML-Based Intrusion Detection Systems

2020

1.
Dietz, K.: Identification and Evaluation of KPIs in SDN via Simulation for Establishing Topology Classifications and Prediction Models, (2020).
[ BibTeX ] [ EndNote ]
 
@mastersthesis{info3-master-2020-14, author = {Dietz, Katharina}, keywords = {sys:hidden:supervisor:nicholas.gray myown sys:hidden:master sys:hidden:completed}, month = 1, school = {University of Würzburg}, title = {Identification and Evaluation of KPIs in SDN via Simulation for Establishing Topology Classifications and Prediction Models}, year = 2020 }
%0 Thesis %1 info3-master-2020-14 %A Dietz, Katharina %D 2020 %T Identification and Evaluation of KPIs in SDN via Simulation for Establishing Topology Classifications and Prediction Models
2.
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) (2020).
[ Abstract ] [ BibTeX ] [ EndNote ]
 
In recent years Software-defined Networking (SDN) has gained increased popularity by reducing the complexity of management operations and increasing the performance of networks. As a result, the number of purchasable SDN devices and their deployment in real world network constantly rises, making a thorough understanding of their behaviors and interactions even more important. In this work we analyze distributed controller architectures via simulation, identify Key Performance Indicators (KPIs), classify various network topologies with respect to their impact on SDN, as well as create a prediction model to estimate the overall performance of the overall SDN ecosystem, which can be used for network planning.
@inproceedings{info3-inproceedings-2020-15, abstract = {In recent years Software-defined Networking (SDN) has gained increased popularity by reducing the complexity of management operations and increasing the performance of networks. As a result, the number of purchasable SDN devices and their deployment in real world network constantly rises, making a thorough understanding of their behaviors and interactions even more important. In this work we analyze distributed controller architectures via simulation, identify Key Performance Indicators (KPIs), classify various network topologies with respect to their impact on SDN, as well as create a prediction model to estimate the overall performance of the overall SDN ecosystem, which can be used for network planning.}, author = {Gray, Nicholas and Dietz, Katharina and Hoßfeld, Tobias}, booktitle = {2020 16th International Conference on Network and Service Management (CNSM)}, keywords = {myown sys:hidden:inproceedings}, title = {Simulative Evaluation of KPIs in SDN for Topology Classification and Performance Prediction Models}, year = 2020 }
%0 Conference Paper %1 info3-inproceedings-2020-15 %A Gray, Nicholas %A Dietz, Katharina %A Hoßfeld, Tobias %B 2020 16th International Conference on Network and Service Management (CNSM) %D 2020 %T Simulative Evaluation of KPIs in SDN for Topology Classification and Performance Prediction Models %X In recent years Software-defined Networking (SDN) has gained increased popularity by reducing the complexity of management operations and increasing the performance of networks. As a result, the number of purchasable SDN devices and their deployment in real world network constantly rises, making a thorough understanding of their behaviors and interactions even more important. In this work we analyze distributed controller architectures via simulation, identify Key Performance Indicators (KPIs), classify various network topologies with respect to their impact on SDN, as well as create a prediction model to estimate the overall performance of the overall SDN ecosystem, which can be used for network planning.

2019

1.
Dietz, K.: Extending the OpenFlow OMNeT++ Suite, (2019).
[ BibTeX ] [ EndNote ]
 
@mastersthesis{info3-praktikum-2019-20, author = {Dietz, Katharina}, keywords = {sys:hidden:praktikum sys:hidden:supervisor:nicholas.gray myown sys:hidden:completed}, month = 2, school = {University of Würzburg}, title = {Extending the OpenFlow OMNeT++ Suite}, year = 2019 }
%0 Thesis %1 info3-praktikum-2019-20 %A Dietz, Katharina %D 2019 %T Extending the OpenFlow OMNeT++ Suite