Honours and Masters project

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.

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:

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:

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:

Despite an increase in the usage of AI models in various domains, the reasoning behind the decisions of complex models may remain unclear to the end-user. Understanding why a model entails specific conclusions is crucial in many domains. A natural example of this need for explainability can be drawn from the use of a medical diagnostic system, where it combines patient history, symptoms and test results in a sophisticated way, estimate the probability that a patient has cancer, and give probabilistic prognoses for different treatment options.

Embodied intelligence represents a rapidly emerging paradigm in robotics where intelligent behaviour arises through the integration of perception, decision-making, action, and interaction within a physical agent operating in the real world. Advances in autonomous robotics, computer vision, sensor fusion, and human-robot interaction have enabled mobile robotic systems to perform increasingly sophisticated tasks in dynamic environments.

Digital identity systems are increasingly used to access online services in areas such as healthcare, banking, education, and e-government. While these systems improve convenience and accessibility, they also raise significant security and privacy concerns, including identity theft, unauthorized data disclosure, user tracking, and large-scale data breaches.

Videos contain rich information about actions, events, interactions, and changes over time. While recent AI models have made strong progress in video understanding, reasoning over complex video content remains challenging, especially when the task requires understanding temporal context or connecting information across different moments.

Large Language Models (LLMs) such as GPT, Llama, Qwen, and Mistral are increasingly used in commercial and academic applications. As more models become available, identifying which model generated a particular response becomes important for copyright auditing, model verification, and AI transparency.

Current fingerprinting methods often rely on manually selected benchmark questions. However, manually designing discriminative questions is time-consuming and may not capture unique behavioral differences between models.

Our research explores novel map representations and projections.

This project seeks to design and trial new map representations for seeing Australian population data sets in new and ideally more effective ways. 

Why is this needed?