Christian Lübben, M. Sc.

Postal address
- Institut für Informatik der
- Technischen Universität München
- Lehrstuhl I8
- Boltzmannstr. 3
- 85748 Garching bei München - Germany
Contact
- Tel: +49 89 289 - 18019
- Fax: +49 89 289 - 18033
- e-Mail: luebben
net.in.tum.de
- Building/Room: MI Building, 03.05.043
Consultation hours
By arrangement
Research
- Smart Space Orchestration
- Distributed Systems
- Machine learning
- Anomaly detection
Teaching
Lab Course iLab2
- The iLab2 is a practical lab course covering selected network topics including IPv6, BGP, IoT Soft-/Hardware, WWW-Security.
- Students can do hands-on network experiments in our designated lab room where they have access to the required hardware.
- In times of COVID restrictions, the course is held entirely virtual with adapted lab experiments.
- In addition, participants create their own small course module about a selected network topic using the same environment.
- Based on student created lab modules, participants can choose topics such as advanced network protocols, selected network attacks, machine learning and IoT protocols.
- Students can do hands-on network experiments in our designated lab room where they have access to the required hardware.
- Roles: Organization, Lecturer
- Semesters: SS2019, WS2019/20, SS2020, WS2020/21
iLabX Block Course at TUM
- The iLabX can also be taken as block course at TUM at the end of each semester. The block course consists of the digital MOOC part and selected exercises from the iLab1 and iLab2 in the physical lab environment at the chair.
- Roles: Lecturer
- Semesters: WS2019/20
Massive Open Online Course (MOOC) iLabX on edX
- The iLabX is designed as Massive Open Online Course (MOOC) about the basics of networking, which is globally available for free on edX: iLabX - The Internet Masterclass.
- A key feature of the iLabX is that relevant networking information is not only taught by video or text, but can be directly experienced as hands-on during the course.
- For this purpose, the vLab was developed which allows participants to run network experiments in a network emulator on their own computer.
- This brings the lab courses already available at TUM (iLab1/2) to a much broader audience, as it removes the requirement of having multiple PCs, routers and other components usually required to form a network.
- Roles: Lecturer, Course Creation
Activities before I started as Research Associate at the chair:
Lecture "Grundlagen Rechnernetze und Verteilte Systeme"
About Me
Christian Lübben is a research associate and PhD student at the chair of Network Architectures and Services at Technical University of Munich (TUM).
He received his Master degree in Informatics from TUM in May 2018. His main area of interest is in Internet of Things and Smart Space research. His research focus lies on optimizing IoT smart spaces using Artificial Intelligence (AI) based data analytics. Challenges include security, usability, resilience, scalability, and performance.
Another field of interest is teaching. With the iLab2 he is advising a practical networking course held at TUM as well as maintaining a Massive Open Online Course (MOOC) aimed at teaching computer network fundamentals using practical exercises in a virtual eLearning environment (iLabX - The Internet Masterclass).
Supervised Theses
Open
Title | Type | Advisors | Year | Links |
IoT Smart Space Service Management | MA | Christian Lübben | 2020 |
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Self-Learning Firewall for Smart Spaces (MA) | MA | Christian Lübben | 2020 |
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Automated Talk Recording with the Videocube | HiWi | Christian Lübben, Marc-Oliver Pahl | 2020 |
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(Reserviert) Survey on AI-based Methods for Network Anomaly Detection | MA, BA | Lars Wüstrich, Christian Lübben, Dr. Holger Kinkelin | 2020 |
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Creation of Data Sets in Automotive Environments for Network Anomaly Detection | MA, IDP | Lars Wüstrich, Christian Lübben, Dr. Holger Kinkelin | 2020 |
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In progress
Student | Title | Type | Advisors | Year | Links |
Nadja Schricker | Creating Traffic Causality Graphs from Network Captures and Application Logic | MA | Lars Wüstrich, Christian Lübben, Holger Kinkelin, Marc-Oliver Pahl | 2021 | |
Mohammed Said Derbel | AI-based anomaly classification | BA | Christian Lübben, Lars Wüstrich, Dr. Holger Kinkelin | 2020 |
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Tobias Wasner | Assisted correction of free text answers | BA | Christian Lübben, Christoph Schwarzenberg, Marc-Oliver Pahl | 2020 |
Finished
Author | Title | Type | Advisors | Year | Links |
Alexander Castendyck | Methods for Performance Anomaly Detection in Distributed, Heterogeneous Systems | MA | Christian Lübben, Marc-Oliver Pahl | 2020 | |
Bassam Jaber | Quantifying Middleware Interoperability via Emulation | MA | Erkin Kirdan, Christian Lübben, Marc-Oliver Pahl | 2020 | |
Florian Bauer | Machine Learning supported IoT Data Modeling | BA | Marc-Oliver Pahl, Christian Lübben | 2020 | |
Simon Schäffner | Continuous Microservice Placement in the IoT | BA | Marc-Oliver Pahl, Christian Lübben | 2020 | |
Benjamin Löhner | Analyzing User Statistics to Give Individual Learning Feedback and Improve Course Content | BA | Christian Lübben, Marc-Oliver Pahl | 2020 | |
Hande Akin | Self-Learning Models for Anomaly Detectin in Smart Spaces | MA | Christian Lübben, Lars Wüstrich, Marc-Oliver Pahl | 2020 | |
Sebastian Vogl | A Reusable Measurement Framework for optimizing IoT Systems | MA | Marc-Oliver Pahl, Christian Lübben, Stefan Liebald | 2019 | |
Marco Eggersmann | Autonomous IoT Service Update and Migration Management | MA | Marc-Oliver Pahl, Christian Lübben, Stefan Liebald | 2019 | |
Julian Ulrich | Self-Adapting IoT User Interfaces | BA | Marc-Oliver Pahl, Christian Lübben, Stefan Liebald | 2019 | |
Sebastian Borchers | Stream connections in P2P Overlays | MA | Marc-Oliver Pahl, Stefan Liebald, Christian Lübben | 2019 | |
Paulius Sukys | IoT Service Modelling | MA | Marc-Oliver Pahl, Stefan Liebald, Christian Lübben | 2019 |
Publications
2020-01-01 | Christian Lübben, Marc-Oliver Pahl, Mohammad Irfan Khan, “Using Deep Learning to Replace Domain Knowledge,” in IEEE ISCC 2020, 2020. [Bib] |