Honours and Masters project

As cities face unprecedented growth, the need for tools that can integrate diverse knowledge sources ranging from geospatial data to the nuanced intuition of urban planners is critical. This research will explore how Bayesian Networks can be adapted to serve as configurable, transparent models that empower decision-makers to weigh alternatives involving complex factors such as development yield, urban zoning, locality to services and infrastructure capacity.

Communication networks show interaction between people over time, and are key to the identification of criminal networks and criminal activity. This project will investigate how future events might be able to be predicted, based on dynamic graphs representing prior interpersonal communications. The project will consider (a) how Graph Neural Networks can best be used for this machine learning task; (b) how visualisation techniques can best depict both known-past and predicted-future events.

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 project focuses on understanding and applying control charts as a tool for monitoring and improving process quality. It involves designing some basic control charts and evaluating their performance in detecting process variations under different conditions. The evaluation will be based on key metrics such as Average Run Length (ARL), false alarm rate, and detection speed, providing insights into the effectiveness of various chart types in maintaining quality standards.

Project Description

Teamwork is a big part of university life, but not all teams work smoothly. Students often face issues such as uneven contributions, unclear communication, or members falling behind. Teaching staff receive a large amount of peer feedback. But the information is often dispersed across multiple reports and can be time-consuming to interpret—particularly in large cohorts. A system that could automatically identify which teams are struggling, and why, would allow educators to offer timely, targeted support.

Organisations continuously face cyberattacks that unfold over multiple stages, often generating vast volumes of intrusion alerts. While modern intrusion detection systems can flag suspicious activities, they typically produce fragmented and low-level alerts that make it difficult for security analysts to understand the overall attack progression and attacker strategies. Manual analysis of these alerts is time-consuming and does not scale to fast-evolving network environments.

This project examines how films produced in Asian markets perform in terms of commercial success and critical recognition using real-world industry data. Students will compile a dataset of films from regions such as Hong Kong, China, South Korea, and Southeast Asia, drawing on publicly available sources to analyse indicators such as production budget, box office revenue, streaming platform release, and awards. Using quantitative data analysis methods, the project aims to identify patterns and factors associated with successful film outcomes.

Multiple master thesis/honour projects on MLLM/ human understanding are available.

Benefits: we aim for publication at top conferences and journals. You will have chance for full PhD scholarship. For those working hard, paid RA opportunities will also be provided.

requirement: WAM>80 and high self-motivation 

This is one of our CSIRO Next Generation AI Graduates Honors projects:

https://www.monash.edu/it/ssc/raise/projects 

Note: You Must be a Domestic Student i.e. Australian or New Zealand Citizen or Australian Permanent Resident  

Project Description

With the widespread adoption of cross-chain bridges and decentralized swapping protocols, an increasing number of money laundering activities leverage multi-chain hopping to obscure the origin and flow of illicit funds. However, most existing public blockchain anti-money laundering datasets focus on transactions within a single blockchain, lacking a systematic characterization of cross-chain fund flows. This limitation significantly constrains the analysis of cross-chain money laundering behaviors and the development and evaluation of related detection methods.