Pegah Golchin, M.Sc.

Pegah Golchin

Short Bio

Pegah Golchin received her M.Sc. degree in Telecommunication Network Engineering from Isfahan University of Technology in 2018. Since 2019 she worked as a computer science research assistant at HHU and she joined the multimedia communication lab as a Ph.D. student in 2021. In her research, she mainly focuses on solving SDN cyber security problems leveraging AI methods.

Research Interests

Applying Machine Learning and Deep Learning approaches in

  • SDN monitoring (traffic classification)
  • Network Intrusion Detection Systems (Network security)
  • SDN data plane (in P4 switches)


  • Software Campus

Open Thesis

I am looking for motivated students who would like to start with their bachelor's or master's theses. Feel free to contact me if you are interested in applying AI to solve the network security problems.

Apart from that, you can also find a list of open topics here.

Supervised Thesis

Year Type Student Title Co-supervisors and notes Status
2023 Master Kexin Wang

Generating adversarial C2 Communication Attacks Leveraging the Shift Distribution Strategy and Online Learning feedback


In progress

2023 Master Chengbo Zhou A Collaborative Machine Learning-based Network Intrusion Detection Architecture in Software Defined Networking  Pratyush Agnihotri


Awarded for the Best Master Thesis at KOM (2023)

2023 Master Yizi Liu In-Network Intrusion Detection System Leveraging Decision Tree Model Inference Fridolin Siegmund Completed
2022 Bachelor Leonard Anderweit In-Network DDoS Attack Detection Leveraging Time Series Data in Programmable Data Planes Ralf Kundel Completed
2021 Master Stefan Stegmueller Generative Adversarial Network-based Robustness Evaluation of Machine Learning Classification Algorithms for DDoS-Attacks External thesis (DE-CIX), Ralf Kundel


Awarded for the Best Master Thesis at KOM (2022)

Supervised Labs and Seminars

Year Type Title Co-supervisors and notes
SoSe23 MMCS Generating Perturbed Network Intrusion Features -
WiSe22 MMCS Machine Learning-based Intrusion Detection Systems in Software-Defined Networking -
SoSe22 MMCS Robustness Evaluation of ML-based NIDS Using Generative Adversarial Networks -
SoSe22 KOM Lab Network Monitoring in SDN Pratyush Agnihotri
WiSe 21 MMCS Encrypted Traffic Classification Leveraging Machine Learning and Deep Learning Models -
WiSe 21 KOM Lab Telcaria Alviu: Network Traffic Monitoring and Profiling Pegah Golchin



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