Data61

Distributed trust AI for solar energy sustainability in commercial settings

This project develops a scheme and prototype for utilising advanced AI technologies to manage and efficient utilise of solar energy. The expected outcomes is an energy sharing trustworthy framework that enables commercial customers reducing energy bills. The project will lead to a sustainable solution for SMEs and contribute to environment protection.

AI-empowered visual recognition system for dairy cow identification, health and behaviour monitoring

This project develops an AI-driven system to identify individual cows, and monitor and detect their health and behaviour, through the use of facial recognition, posture analysis, and thermal imaging. The expected outcome is early detection of illness and abnormal activity of cows. This project will support productivity, reduce losses, and improve animal welfare in the dairy industry.

Dark web analytics for ransomware threat actor profiling

This project investigates active ransomware groups and attempts to identify their key characteristics, attack signatures, victims, and stolen data. The expected outcome is development of a dataset to serve as an essential resource. The project will potentially provide more knowledge on ransomware groups, their tactics, techniques, and procedures for launching attacks.

Building responsible AI: Co-designing knowledge transfer solutions using generative AI

This project will bridge the gap between the principles of responsible AI and their measurable practice, by developing AI knowledge tools to empower end-user community groups. The expected outcomes are to build insights and develop AI tools for information dissemination and knowledge transfer. The potential benefit is to produce responsible AI solutions to help disability groups connect, communicate, share and learn.

Improving GNSS measurements of Australia’s deformation using machine learning  

This Project will improve the accuracy of estimates of Australia’s 3D motion and deformation using machine learning methods. This will apply new methods to hundreds of Global Navigation Satellite System (GNSS) sites to improve understanding of Australia’s vertical land motion and sea level research. This may improve satellite positioning products used by Australian industry, government and researchers. 

Clinical lab automation with AI human robot interface

This Project will develop an AI-based robotic programming interface based on large-language model that allows practitioners, regardless of their technical expertise, to efficiently program and control robots. The expected outcomes are to improve efficiency in designing and deploying clinical lab automation and to expand the use of robotics within laboratories. This may lead to improvements with workflow for clinical lab automation, particularly during high-demand situations like pandemic outbreaks.

Cyber security risk mitigation for sensor data integrity

This Project will investigate cyber security risk mitigation approaches to secure the integrity of sensor information feeding into critical infrastructure operational systems and digital twins. The expected outcome is the development of guidelines for implementing robust cyber security measures. This may enhance resilience against cyber threats and ensure the integrity of decision-making processes.​

Resilient and quantum-safe threshold cryptography

This Project will design quantum-safe threshold encryption and/or authentication algorithms. The expected outcome is the design of methods, techniques and their software prototype to implement quantum-safe threshold encryption and/or authentication algorithms. The potential benefit is to enhance the security of Australian critical infrastructures, safeguarding them against quantum attacks.

Enabling Post-Quantum Cryptography (PQC) Migration

This Project develops techniques for the migration to post-quantum cryptography (PQC) to secure critical infrastructure from quantum attacks. The expected outcome is the design of methods, techniques and their prototype to implement trusted PQC migration. The potential benefit is to enhance the security of Australian critical infrastructures against quantum attacks

Enhancing cybersecurity with AI and Large Language Models 

This project will explore the integration of artificial intelligence (AI) and large language models (LLMs) to predict organisational cybersecurity risks and mitigate threats in advance. The expected outcomes are an enhanced cybersecurity framework, better threat intelligence techniques and user-centric designs, and an adaptable solution. This may help businesses to identify cyber risks and prevent cyber incidents prior to happening and avoid financial losses and brand damage.