Topic Background:
In the era of Industry 4.0 and Artificial Intelligence, information models are crucial for improving efficiency and reducing costs by ensuring and integrating complex systems and components. The Information Modelling Framework (IMF) is a language designed to model the information and data of industrial assets, particularly engineered systems, enabling seamless interoperability and intelligent reasoning across various systems.
As IMF becomes integral to Industry 4.0, ensuring the reliability of the tool/software for IMF through testing methods like fuzz testing (fuzzing) becomes critical. Fuzz testing provides an effective way to identify vulnerabilities and unexpected behaviours for IMF’s tool by inputting random or malformed data and information.
This approach will help improve the robustness and security of IMF-based tools across various industries/usecases.
Potential Research Question/Direction:
How can fuzz testing be effectively applied to the Information Modelling Framework (IMF) to enhance its reliability and security across diverse industrial applications?
This is a general question, it can be modified depending on student’s interest.
Expected Tasks:
Literature Review: Conduct a review of existing research on information modelling and fuzz testing techniques.
Understanding and Learning: Gain in-depth knowledge of the Information Modelling Framework and fuzz testing methodologies
Implementation: Develop and implement a fuzz testing strategy for the selected IMF-based tool.
Experimental Evaluation: Conduct experiments to test the selected IMF-based tool using the developed fuzz testing strategy, systematically documenting the findings.
Analysis and Conclusion: Analyse the results, identify common vulnerabilities or weaknesses, propose improvements, and finalise the thesis.
The thesis is suitable for students who have interest within fuzz testing, semantic technologies, and some extra knowledge in systems engineering.