- Applications close
- 31 July 2020
What you'll receive
The award is based in QUT's Gardens Point Campus and is to be used to support living expenses.
There is a stipend of $50,000 per year (indexed annually) for up to three years of full time study.
To be considered for this project, you should hold a Bachelor degree with Honours 1 or 2A, a Master degree by research, or an equivalent qualification within the broad fields of information technology.
You must also be a citizen of one of the following countries:
- United States of America
- United Kingdom
- New Zealand.
We may consider other factors when deciding the appropriate candidate, including:
- previous study
- other research experience
- research publications
- referees’ reports
- relevant work experience.
How to apply
If you want to apply for the scholarship, please refer to our application guide. This scholarship will remain open until 31 July 2020.
For further information, or to discuss this research project, please contact Associate Professor Yue Xu. Your email should include your:
- up-to-date CV
- full academic transcript
- brief summary of your career.
About the scholarship
Critical infrastructure control systems are becoming more connected and vulnerabilities within these systems are susceptible to being exploited by malicious actors. This project will seek to investigate the application of machine learning to automate two activities that will increase the cyber security of industrial control systems.
Most vulnerability assessment is based on manual code analysis by security experts. This project will seek to automate the assessment and code analysis of operational technology (OT) system software using machine learning and data mining techniques. The aim of this assessment is to identify existing and potential security vulnerabilities and predict the impact of these vulnerabilities.
Firewalls and current intrusion detection systems concentrate on protecting the system perimeter but do little to identify successful attacks that have penetrated the perimeter. Thus, successful attacks are not discovered until harm has occurred. This project will investigate the use of machine learning in attack discovery.
This PhD project will contribute towards the vision of an automatic OT cyber security vulnerability prediction and attack discovery system that will provide automated ongoing vulnerability assessment and asset monitoring for OT systems.
This PhD project is supported by Cyber Security Cooperative Research Centre (CSCRC) along with:
- Tata Consultancy Services (TCS) Australia as the industry partner
- Queensland University of Technology (QUT) as the research partner
- Griffith University as an external collaborative partner for this project.
The supervisory team includes:
- Associate Professor Yue Xu from QUT
- Associate Professor Ernest Foo from Griffith University
- Dr. Praveen Gauravaram from Tata Consultancy Services.