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With 11,500 employees worldwide, AVL is the world's largest independent company for the development, simulation and testing of powertrain systems (hybrid, combustion engine, transmission, electric drive, batteries, fuel cell and control technology) for passenger cars, commercial vehicles, construction, large engines and their integration into the vehicle. Connected vehicles are the driving force of many mobility innovations for improved efficiency, comfort and safety. Changing vehicle architectures and upcoming regulatory requirements require efficient ways of collecting security relevant logs and performing anomaly detection for the identification of cyber security incidents.


  • Identify security relevant log sources inside a modern vehicle
  • Define a logging concept including the characteristics of a modern vehicle architecture
  • Analyze the potential of different anomaly detection approaches (machine learning / AI versus pattern based) considering the potential limitations of embedded systems with regards to computing power and memory
  • Define a concept for distributed anomaly detection architectures in combination with backend / cloud infrastructures
  • Proof-of-concept implementation and demonstration

Fields of study

  • Informatics/Computer Science
  • IT/Information Security


  • Knowledge in automotive security a plus
  • Experiences in embedded systems, Log Management, SIEM
  • Strong analytical and problem solving skills
  • Strong programming skill in Python, C

Remuneration: The successful completion of the thesis is remunerated with a one-time fee of EUR 2,600 before tax.

​​​​According to the Austrian Employment of Foreign Nationals Act it is unfortunately not possible to assign graduate work to third-country citizens (Non-EU citizens) who study at a university abroad.


Thomas Schober
Lead Engineer Cyber Security / DSF
Tel.: +43 664 6109826