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.

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? 

Evaluation benchmarks are a foundational component of artificial intelligence (AI) research, providing standardized ways to measure and compare the capabilities of AI systems. Benchmarks such as MMLU, GSM8K, HumanEval, and HellaSwag have been instrumental in tracking progress in large language models and related systems. However, benchmark usefulness is not static.

Large Language Models (LLMs) are increasingly used to automatically evaluate other AI systems in tasks such as writing, reasoning, and question answering. This approach called LLM-as-a-Judge is now widely used in research benchmarks and AI development pipelines.