Open Topics

We offer multiple Bachelor/Master theses, Guided Research projects and IDPs in the area of data mining/machine learning. Some of the open topics are listed below.

If you are interested in a thesis or a guided research project, please send your CV and transcript of records to Prof. Stephan Günnemann via email and we will arrange a meeting to talk about the potential topics.

Anomaly detection in dynamic networks

Type: Interdisciplinary project (IDP)

Prerequisites:

  • Strong machine learning knowledge
  • Good programming skills
  • (Preferred) experience with probabilistic graphical models (e.g. stochastic block model)
  • (Preferred) experience with node embedding methods (e.g. DeepWalk, node2vec)

Description: The growing complexity of communication networks makes their analysis and maintenance increasingly challenging for human administrators. For example, campus, home, and enterprise computer networks all suffer from badly secured Internet of Things (IoT) devices, hacker attacks or simply outdated software. Developing automated tools for detection, analysis and mitigation of anomalies (e.g. hardware failures or security threats) in such networks is both an open research problem and an important practical challenge. 

In this IDP, you will work together with the Chair of Communication Networks and our group on developing machine learning models for anomaly detection in dynamic communication networks. You will use methods such as node embedding methods [1], probabilistic graphical models [2] or community detection algorithms [3].

Contact: Patrick Kalmbach

References:

  1. DeepWalk: Online Learning of Social Representations
  2. Statistical clustering of temporal networks through a dynamic stochastic block model
  3. An efficient and principled method for detecting communities in networks