Teaching - Lehrstuhl für Informatik III

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

Lectures (Teaching Assistant)

Leistungsbewertung verteilter Systeme (LBVS) (SS23)
Leistungsbewertung verteilter Systeme (LBVS) (SS22)
Steuerungsprinzipien moderner Kommunikationssysteme (SKS) (WS20/21)

Seminar Talks (Tutor)

User-centric Communication Networks (UCN) (WS22/23)

M. E.: MimicNet: Fast Performance Estimates for Data Center Networks with Machine Learning (based on the SIGCOMM Paper by Zhang et al.)

User-centric Communication Networks (UCN) (SS22)

P. A.: Graph Neural Networks for Malware Detection

User-centric Communication Networks (UCN) (WS21/22)

Y. S.: Firewall Fingerprinting
A. N.: Malware Fingerprinting

User-centric Communication Networks (UCN) (SS21)

M. S.: QoE and Privacy
B. H.: QoE and Security

User-centric Communication Networks (UCN) (WS20/21)

P. K.: Reinforcement Learning for Traffic Routing
M. E.: Reinforcement Learning for Network Security

Next Generation Networks (NGN) (SS20)

L. R.: SDN in the Datacenter: Usecases and Challenges
A. R.: SDN outside the Datacenter: SD-WAN and Co.

Unassigned Theses/Student Projects

Supervised Theses

Performance Evaluation of Feature Representation Transfer Approaches for Video QoE Estimation Across Different Networks Ceanuri Devesa, Jaione Karmele PhD thesis, University of Würzburg. (2022, July).
- [ BibTeX ]
- [ EndNote ]
@mastersthesis{info3-master-2022-5, author = {Ceanuri Devesa, Jaione Karmele}, keywords = {ucn}, month = 7, school = {University of Würzburg}, title = {Performance Evaluation of Feature Representation Transfer Approaches for Video QoE Estimation Across Different Networks}, year = 2022 }

%0 Thesis %1 info3-master-2022-5 %A Ceanuri Devesa, Jaione Karmele %D 2022 %T Performance Evaluation of Feature Representation Transfer Approaches for Video QoE Estimation Across Different Networks
Building a GAN from Scratch for Synthesizing Data Samples for the Use Case of Browser Fingerprinting Sichermann, Marleen PhD thesis, University of Würzburg. (2022, March).
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
With the recent rise of Machine Learning and especially Deep Learning, gathering enough data is a challenging task. Generative Adversarial Networks (GANs) have emerged to synthesize fake data from real data, by employing two neural networks that interact with each other, i.e., a discriminator and a generator, where the generator tries to trick the discriminator into classifying a fake sample as real. GANs are usually employed on images, however, this has recently been adopted in other research areas. This thesis investigates the capability of GANs to generate fake samples of network traffic, e.g., for data augmentation to balance out datasets (for intrusion detection, for example).

@mastersthesis{info3-bachelor-2022-7, abstract = {With the recent rise of Machine Learning and especially Deep Learning, gathering enough data is a challenging task. Generative Adversarial Networks (GANs) have emerged to synthesize fake data from real data, by employing two neural networks that interact with each other, i.e., a discriminator and a generator, where the generator tries to trick the discriminator into classifying a fake sample as real. GANs are usually employed on images, however, this has recently been adopted in other research areas. This thesis investigates the capability of GANs to generate fake samples of network traffic, e.g., for data augmentation to balance out datasets (for intrusion detection, for example).}, author = {Sichermann, Marleen}, keywords = {ucn}, month = 3, school = {University of Würzburg}, title = {Building a GAN from Scratch for Synthesizing Data Samples for the Use Case of Browser Fingerprinting}, year = 2022 }

%0 Thesis %1 info3-bachelor-2022-7 %A Sichermann, Marleen %D 2022 %T Building a GAN from Scratch for Synthesizing Data Samples for the Use Case of Browser Fingerprinting %X With the recent rise of Machine Learning and especially Deep Learning, gathering enough data is a challenging task. Generative Adversarial Networks (GANs) have emerged to synthesize fake data from real data, by employing two neural networks that interact with each other, i.e., a discriminator and a generator, where the generator tries to trick the discriminator into classifying a fake sample as real. GANs are usually employed on images, however, this has recently been adopted in other research areas. This thesis investigates the capability of GANs to generate fake samples of network traffic, e.g., for data augmentation to balance out datasets (for intrusion detection, for example).
Experimental Evaluation Tool for Exploring User-based Active Learning for ML-based Browser and URL Detection Glauer, Steven PhD thesis, University of Würzburg. (2022, September).
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
While Machine Learning (ML) is gaining more and more popularity, the actual incorporation into running systems is still lacking, e.g., due to missing trust of network operators. To build this trust, the goal of this work is to incorporate an active learning approach into an already existing ML-approach, that opens up the possibility of making ML-based decisions more explainable and allows to incorporate feedback of the administrator, e.g.,to retrain ML models with the adjusted decisions.

@mastersthesis{info3-bachelor-2021-22, abstract = {While Machine Learning (ML) is gaining more and more popularity, the actual incorporation into running systems is still lacking, e.g., due to missing trust of network operators. To build this trust, the goal of this work is to incorporate an active learning approach into an already existing ML-approach, that opens up the possibility of making ML-based decisions more explainable and allows to incorporate feedback of the administrator, e.g.,to retrain ML models with the adjusted decisions.}, author = {Glauer, Steven}, keywords = {ucn}, month = 9, school = {University of Würzburg}, title = {Experimental Evaluation Tool for Exploring User-based Active Learning for ML-based Browser and URL Detection}, year = 2022 }

%0 Thesis %1 info3-bachelor-2021-22 %A Glauer, Steven %D 2022 %T Experimental Evaluation Tool for Exploring User-based Active Learning for ML-based Browser and URL Detection %X While Machine Learning (ML) is gaining more and more popularity, the actual incorporation into running systems is still lacking, e.g., due to missing trust of network operators. To build this trust, the goal of this work is to incorporate an active learning approach into an already existing ML-approach, that opens up the possibility of making ML-based decisions more explainable and allows to incorporate feedback of the administrator, e.g.,to retrain ML models with the adjusted decisions.
Plausible Performance Prediction of Simulated SDN-enabled Networks via Machine Learning Ebner, Markus PhD thesis, University of Würzburg. (2021, July).
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
In recent years, Software-Defined Networking (SDN) emerged as an innovative network architecture and gained increasing popularity due to its centralized network management. However, this central point induces a potential bottleneck and possible single-point-of-failure to the network. Therefore, so-called distributed architectures come into play, which keep the logical view centralized, but physically distributed. The goal of this thesis is to evaluate two opposing approaches and predict their performance via Machine Learning (ML). The simulation will be conducted on basis of a preexisting framework based on OMNeT++, which already implements two distributed SDN-architectures, ready for simulation.

@mastersthesis{info3-bachelor-2021-9, abstract = {In recent years, Software-Defined Networking (SDN) emerged as an innovative network architecture and gained increasing popularity due to its centralized network management. However, this central point induces a potential bottleneck and possible single-point-of-failure to the network. Therefore, so-called distributed architectures come into play, which keep the logical view centralized, but physically distributed. The goal of this thesis is to evaluate two opposing approaches and predict their performance via Machine Learning (ML). The simulation will be conducted on basis of a preexisting framework based on OMNeT++, which already implements two distributed SDN-architectures, ready for simulation.}, author = {Ebner, Markus}, keywords = {ucn}, month = 7, school = {University of Würzburg}, title = {Plausible Performance Prediction of Simulated SDN-enabled Networks via Machine Learning}, year = 2021 }

%0 Thesis %1 info3-bachelor-2021-9 %A Ebner, Markus %D 2021 %T Plausible Performance Prediction of Simulated SDN-enabled Networks via Machine Learning %X In recent years, Software-Defined Networking (SDN) emerged as an innovative network architecture and gained increasing popularity due to its centralized network management. However, this central point induces a potential bottleneck and possible single-point-of-failure to the network. Therefore, so-called distributed architectures come into play, which keep the logical view centralized, but physically distributed. The goal of this thesis is to evaluate two opposing approaches and predict their performance via Machine Learning (ML). The simulation will be conducted on basis of a preexisting framework based on OMNeT++, which already implements two distributed SDN-architectures, ready for simulation.
Evaluation of Data Augmentation for Machine Learning on Network Traffic Khelloqi, Sanaa PhD thesis, University of Würzburg. (2021, April).
- [ BibTeX ]
- [ EndNote ]
@mastersthesis{info3-bachelor-2020-9, author = {Khelloqi, Sanaa}, keywords = {ucn}, month = 4, school = {University of Würzburg}, title = {Evaluation of Data Augmentation for Machine Learning on Network Traffic}, year = 2021 }

%0 Thesis %1 info3-bachelor-2020-9 %A Khelloqi, Sanaa %D 2021 %T Evaluation of Data Augmentation for Machine Learning on Network Traffic

Katharina Dietz M. Sc.

Lectures (Teaching Assistant)

Seminar Talks (Tutor)

Unassigned Theses/Student Projects

Supervised Theses

Hinweis zum Datenschutz

Hinweis zum Datenschutz

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