Automatic identification of component and software in industrial environments

Join us in investigating complex IIOT components and software applications in industrial environments for your BSc/MSc thesis. Today's production facilities are very complex, with different stakeholders pursuing a common goal from different perspectives (manufacturer, installer and operator). On the other hand, many legacy systems are still in use, but their lifespan is not yet foreseeable and they will continue to be used in the near future. This ongoing transition from a completely isolated network, with components that have no safeguards against malicious attacks, to a fully automated I4.0 setup poses many risks that need to be managed. Contribute to enhancing risk management strategies for a safer and more connected future. Dive into the world of industrial IoT, make a meaningful impact, and shape a secure digital industrial landscape.

Student Target Groups:

  • Students of ICE/Telematics;
  • Students of Computer Science;
  • Students of Software Engineering.

Thesis Type:

  • Master Thesis / Bachelor Thesis

Goal and Tasks:

The aim of this bachelor thesis is to identify which components and software are used in a specific production system. Therefore, the concept of fingerprinting, based on the analysis of specific properties such as message format or timing, will be used to identify hardware and software components. The challenge here is proprietary communication channels and their sometimes very limited bandwidth, so the initial focus is on passive fingerprinting methods due to their non-invasiveness. In addition, the potential of active fingerprinting approaches, which involve triggering targeted requests on the communication channel, should also be explored.

  • Thorough literature research on the topic;
  • Select/develop suitable scanning methods;
  • Develop suitable analyser methods for the fingerprinting;
  • Design and conduct experiments to investigate the applicability of Federated Learning (FL) in such an environment;
  • Summarize the results in a written report, and prepare an oral presentation.

Recommended Prior Knowledge:

  • Programming skills in c, c++;
  • Prior experience with bus systems and network architectures.
  • Interest in the topic.


  • a.s.a.p.