Christian Lübben, M. Sc.

Research Associate
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Postal address

  • Institut für Informatik der
  • Technischen Universität München
  • Lehrstuhl I8
  • Boltzmannstr. 3
  • 85748 Garching bei München - Germany

Contact

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.
  • 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, Marc-Oliver Pahl 2020 Pdf
Self-Learning Firewall for Smart Spaces (MA) MA Christian Lübben, Marc-Oliver Pahl 2020 Pdf
Automated Talk Recording with the Videocube HiWi Christian Lübben, Marc-Oliver Pahl 2020 Pdf
(Reserviert) Survey on AI-based Methods for Network Anomaly Detection MA, BA Dr. Holger Kinkelin, Christian Lübben 2020 Pdf

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]