Dr. rer. nat. Alexander Motzek

- Research Assistent -


Institut für Informationssysteme
Universität zu Lübeck
Ratzeburger Allee 160 ( Gebäude 64 - 2.OG )
D-23562 Lübeck

email : Öffnet ein Fenster zum Versenden einer E-Mail E-Mail for Alexander Motzek

Curriculum Vitae



  • October 2016 - PhD in Computer Science, Artificial Intelligence
  • January 2014 - Master of Science in Computer Science and Engineering, Hamburg University of Technology.
  • January 2012 - Bachelor of Engineering in Electrical- & Information Engineering, Hamburg University of Applied Science.
  • March 2010 - Electronics Technician for Automation Technology, IHK Hamburg, Siemens AG.

PhD Thesis: Indirect Causes, Dependencies and Causality in Bayesian Networks

Modeling causal dependencies in complex or time-dependent domains often demands cyclic dependencies. Such cycles arise from local points of views on dependencies where no singular causality is identifiable, i.e., roles of causes and effects are not universally identifiable. Modeling causation instead of correlation is of utmost importance, which is why Bayesian networks are frequently used to reason under uncertainty. Bayesian networks are probabilistic graphical models and allow for a causal modeling approach with locally specifiable and interpretable parameters, but are not defined for cyclic graph structures. If Bayesian networks are to be used for modeling uncertainties, cycles are eliminated with dynamic Bayesian networks, eliminating cycles over time. However, we show that eliminating cycles over time eliminates an anticipation of indirect influences as well, and enforces an infinitesimal resolution of time. Without a ``causal design,'' i.e., without representing direct and indirect causes appropriately, such networks return spurious results.


In particular, the main novel contributions of this thesis can be summarized as follows. By considering specific properties of local conditional probability distributions, we show that a novel form of probabilistic graphical models rapidly adapts itself to a specific context at every timestep and, by that, correctly anticipates indirect influences under an unrestricted time granularity, even if cyclic dependencies arise.


For more information on this topic, current research activities and supplementary material, please visit:





Research Interests

  • Probabilistic Graphical Models
  • Bayesian Networks
  • Computer Vision
  • Informationsecurity


  • Karsten Martiny, Alexander Motzek, Ralf Möller: Formalizing Agents’ Beliefs for Cyber-Security Defense Strategy Planning
    in: International Joint Conference - CISIS 15 and ICEUTE 15, 8th International Conference on Computational Intelligence in Security for Information Systems / 6th International Conference on European Transnational Education, 2015, June, Springer International Publishing, Burgos, Spain, Vol.369, p.15-25
    DOI BibTeX
  • Alexander Motzek, Ralf Möller, Mona Lange, Samuel Dubus: Probabilistic Mission Impact Assessment based on Widespread Local Events
    in: NATO IST-128 Workshop on Cyber Attack Detection, Forensics and Attribution for Assessment of Mission Impact, 2015, June, IST, Istanbul, Turkey
  • Alexander Motzek, Ralf Möller: Indirect Causes in Dynamic Bayesian Networks Revisited
    in: IJCAI 2015: 24th International Joint Conference on Artificial Intelligence, 2015, July, AAAI, Buenos Aires, Argentina, p.703-709
    Website BibTeX
  • Alexander Motzek, Ralf Möller: Exploiting Innocuousness in Bayesian Networks
    in: AI 2015: Advances in Artificial Intelligence - Proceedings of the 28th Australasian Conference, Canberra, ACT, Australia, November 30 - December 4, 2015, 2015, p.411-423
    DOI BibTeX
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  • Alexander Motzek, Christina Geick, Ralf Möller: Semantic Normalization and Merging of Business Dependency Models
    in: CBI 2016: 18th IEEE Conference on Business Informatics, Paris, France, August 29 - September 1, 2016
  • Gustavo Gonzalez Granadillo, Alexander Motzek, Joaquin Garcia-Alfaro, Hervé Debar: Selection of Mitigation Actions Based on Financial and Operational Impact Assessments
    in: ARES 2016: 11th International Conference on Availability, Reliability and Security, Salzburg, Austria, August 31 - September 2, 2016, p.137-146
    DOI BibTeX
  • Alexander Motzek, Ralf Möller: Probabilistic Mission Defense and Assurance
    in: NATO IST-148 Symposium on Cyber Defence Situation Awareness, NATO IST-148, Sofia, Bulgaria, October 3-4, 2016
  • Gustavo Gonzalez Granadillo, Ender Alvarez, Alexander Motzek, Matteo Merialdo, Joaquin Garcia-Alfaro, Hervé Debar: Towards an Automated and Dynamic Risk Management Response System
    in: NordSec 2016: 21st Nordic Conference on Secure IT Systems, Oulu, Finland, November 2 - 4, 2016, Springer, LNCS, Vol.10014, p.37-53
    DOI BibTeX
  • Alexander Motzek, Ralf Möller: Context- and Bias-Free Probabilistic Mission Impact Assessment
    in: Computers & Security, DOI j.cose.2016.11.005, 2016, Vol.65, p.166-186, ISSN 0167-4048
    DOI BibTeX
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  • Motzek, Alexander, Möller, Ralf: Indirect Causes in Dynamic Bayesian Networks Revisited
    in: Journal of Artificial Intelligence Research (JAIR), 2017, Vol.59, p.1-58
    Website BibTeX
  • Alexander Motzek, Gustavo Gonzalez-Granadillo, Herve Debar, Joaquin Garcia-Alfaro, Ralf Möller: Selection of Pareto-efficient Response Plans based on Financial and Operational Assessments
    in: EURASIP Journal on Information Security, 2017, Juli, p.2017:12
    Website BibTeX
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Student Theses

Open Theses

Supervised Theses

  • Trong Thang Tran. Verbesserung eines auf Fourier Klassifikators unter Verwendung von Deskriptoren basierenden maschinellem Lernen, Master's Thesis, Hamburg University of Technology, September 2014.