Bachelor | Master | PhD – Thesis Supervision


STUDENTS INTERESTED to do an INTERNSHIP or pursue a THESIS (B.Sc., DI, M.Sc., or Ph.D.) are advised to directly CONTACT OUR RESEARCHERS as listed below.

Security and Privacy in Machine Learning

Below is a list of topics related to security and privacy aspects of Machine Learning / Data Mining / Artificial Intelligence. If you are interested, please contact Rudolf Mayer

Adversarial Machine Learning:

A good overview talk (in German) into Adversarial Machine Learning is given by Konrad Rieck: “Sicherheitslücken in der künstlichen Intelligenz”:  

Privacy-preserving Machine Learning/Data Mining

Privacy-preserving analysis of data is becoming more relevant with the increasing amount of personal data being gathered. Several different approaches aiming at this problem exist, e.g.:

Watermarking / fingerprinting of datasets

  • Goal: evaluation of schemes for their robustness of attacks, vs. their data utility, e.g. measured by effectiveness in machine learning tasks

Digital Sustainability and Resilience

  • Demonstrate a complex yet resilient component based infrastructure by implementing a (Netflix) Chaos Monkey demonstration setup with Docker/Kubernetes  components. Edgar Weippl
  • Runtime analysis of selected open source system(s): use debug interface of (Java) virtual machine to analyse runtime behaviour, draw a “code-landscape” and identify processes. Edgar Weippl
  • Analyse the complexity of selected Open Source projects following established complexity- and entropy-indicators. This might include static code analysis and analysis of code changes (source code repository). Edgar Weippl
  • Implement a dynamic simulation of an organisation and deduce efficiency “bottle-necks” using queuing theory. Edgar Weippl

Security for Cyber Physical Production Systems

  • Network Security in CPPS. Which tools (like metasploit) are readily available for pentesting CPPS (instead of business systems)? Edgar Weippl
  • Forensics Analysis in CPPS. Which tools exist that can be used to analyse CPPS? Are there guidelines on how to proceed? Edgar Weippl
  • Secure Software Engineering Processes in CPPS. Edgar Weippl

Combinatorial Testing for Information Security



Fileformat Security

Internet of Things (IoT) Security and Privacy

  • Update mechanisms for constrained IoT systems
  • Secure embedded operating systems for IoT applications: Georg Merzdovnik
  • IoT network protocol security analysis
  • Privacy leaks and protections for IoT devices and protocols
  • Lightweight intrusion detection for IoT systems: Georg Merzdovnik
  • Efficient implementation of security and cryptography for IoT
  • Moving-target defenses in IoT environments
  • Resilient communication protocols for IoT

Internet of Things (IoT) – Constrained Networks

  • Integration of RIOT OS into SecureWSN
    – TinyIPFIX for Data Gathering
    – Aggregation Support
  • Implementation of security solutions
  • Optimization of system lifetime using energy harvesting mechanisms
  • Extention of visualization mechanisms
  • Integration of warning system for unnormal behavior (e.g., wrong values or to high packet loss rate)
  • Integration of “Trust-Check” for devices
  • Establishment of bidirectional communication (e.g., for updating purposes of intervals)

Topic description available under

Cyber-Physical System Security

Mobile Security and Privacy

Network Security

Privacy Enhancing Technologies

Cloud Security

Research in Information Security

  • Comparing military research and academic research – analysis of de-classified content: Edgar Weippl
  • Analysis of security in standardization processes: Johanna Ullrich

Software Security

System Security

Usable Security

  • Usable Security. Developing a guideline of how to do research correctly: Edgar Weippl


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