Automated Penetration Testing using Improve Deep Reinforcement Learning Techniques

The University of Queensland

23 December 2022
16 January 2023
Engineering and Technology

Supervisor: Associate Professor Dan Kim

This project will investigate state-of-the-art techniques in penetration testing. The candidate will work on 1) literature reviews on autonomous and automated pen-testing, 2) reviews on reinforcement learning and its advances, 3) develop an improved autonomous and automated pen-testing using an improved reinforcement learning, and 4) implement the proposed approach and evaluate the performance of the proposed approach.

Selection criteria:

Your application will be assessed on a competitive basis.

We take into account your:

  • previous academic record
  • publication record
  • honours and awards
  • employment history

A working knowledge of computer science and cybersecurity would be of benefit to someone working on this project.

You will demonstrate academic achievement in the field/s of cybersecurity and the potential for scholastic success.

A background or knowledge of computer science, cybersecurity, and pen testing is highly desirable.


You're eligible if you meet the entry requirements for a higher degree by research.

Learn more or apply
All information about this funding has been collected from and belongs to the funding organization
29 May 2023