Honours and Masters project

Please note that this Honours and Masters Project topic is offered exclusively at our Monash Malaysia campus and is not available at the Clayton campus.

This project is not just another IDS project. It sits at the intersection of four powerful areas:

So you will deal with:

Please note that this Honours and Masters Project  topic is offered exclusively at our Monash Malaysia campus and is not available at the Clayton campus.

Can a Transformer understand a software patch and predict whether it truly fixes a vulnerability, introduces a new weakness, or leaves the system still exploitable?

This is much more specific than normal vulnerability detection.

Instead of asking:

“Is this code vulnerable?”

we ask:

This is a Winter Student Research Internship 2026 advert (and already filled).

However it will convert to honours /minor thesis project after the break. If you are interested in this research as a thesis particularly the 3D component, please contact me. 

(Note that Winter and summer student internships must be  applied for here:

https://www.monash.edu/study/fees-scholarships/scholarships/summer-winter)

__________

Background:

Cyber Threat Intelligence (CTI) plays a vital role in today's cybersecurity landscape by collecting and analysing data about current and potential threats, providing insights to better understand, mitigate and respond in this ever-evolving environment. A core component of CTI is the identification of adversarial Tactics, Techniques, and Procedures (TTPs), which describe how attackers operate at a strategic and operational level.

Background and motivation

Feedback is central to student learning, but feedback does not automatically lead to improvement. Students need opportunities to understand, evaluate, and act on feedback, while teachers and teaching teams need to design feedback practices that are clear, actionable, timely, and connected to learning activities.

Quantum-accelerated Bayesian network (BN) structure learning asks whether quantum algorithms can speed up the combinatorial search over directed acyclic graphs while still making realistic systems assumptions.

Bayesian network (BN) inference—computing posterior probabilities given evidence—is a core task in probabilistic reasoning, but it becomes computationally expensive as networks grow in size or treewidth increases. Quantum-accelerated BN inference explores whether quantum algorithms and quantum circuit representations can provide practical advantages for approximate inference and sampling, while still making realistic assumptions about data access, noise, and limited quantum resources.

TBA

TBA