Defending AI based FinTech Systems against Model Extraction Attacks. This project aims to develop new methods for defending artificial intelligence (AI) based FinTech systems from highly potent and insidious model extraction attacks whereby an adversary can steal the AI model from the system to cause intellectual property (IP) violation, business advantage disruption, and financial loss. This can be achieved by examining various attack models, creating active and utility-preserving defences, and ....Defending AI based FinTech Systems against Model Extraction Attacks. This project aims to develop new methods for defending artificial intelligence (AI) based FinTech systems from highly potent and insidious model extraction attacks whereby an adversary can steal the AI model from the system to cause intellectual property (IP) violation, business advantage disruption, and financial loss. This can be achieved by examining various attack models, creating active and utility-preserving defences, and inventing non-removable watermarks on AI models. The outcomes are new tools for securing AI-based FinTech systems before deployment and tools for IP violation forensics post-deployment. Such capabilities are beneficial by improving the security and safety of FinTech systems and other nationally critical AI systems.Read moreRead less
Tuning parallel applications on software-defined supercomputers. Supercomputers are used by many Australian industries and laboratories to make better products and perform critical predictions, and it is essential that codes operate efficiently. This project aims to assist programmers in identifying performance bottlenecks in their code quickly and easily. The project expects to supersede the current methods, which are often complex and time-consuming, by developing innovative software tools and ....Tuning parallel applications on software-defined supercomputers. Supercomputers are used by many Australian industries and laboratories to make better products and perform critical predictions, and it is essential that codes operate efficiently. This project aims to assist programmers in identifying performance bottlenecks in their code quickly and easily. The project expects to supersede the current methods, which are often complex and time-consuming, by developing innovative software tools and techniques. The expected outcomes include novel software, verified by industry partners in real world case studies, ranging from life sciences to hypersonic transport. This should provide significant benefits, including the capacity for Australian industries to access world-class supercomputing technology.Read moreRead less
Rigorous Privacy Compliance in Modern Application Ecosystems. Modern network applications such as mobile applications and browser extensions have become the primary gateways for consumers to access the Internet in today’s digital landscape. This project aims to address privacy issues in these ecosystems by developing a new privacy-compliance assessment framework. The framework will evaluate the current privacy practices of application ecosystems, enabling users and developers in Australia and wo ....Rigorous Privacy Compliance in Modern Application Ecosystems. Modern network applications such as mobile applications and browser extensions have become the primary gateways for consumers to access the Internet in today’s digital landscape. This project aims to address privacy issues in these ecosystems by developing a new privacy-compliance assessment framework. The framework will evaluate the current privacy practices of application ecosystems, enabling users and developers in Australia and worldwide to reliably identify potential privacy risks and issues on their applications. The intended outcomes should endow data controllers with the capability of evidencing their compliance of data protection legislations such as Australia Privacy Act 1988 and EU General Data Protection Regulation (GDPR).Read moreRead less
Embedding Enterprise Systems in IoT Fog Networks through Microservices. The project will enable automated re-engineering of enterprise systems, to allow them to reused in Internet-of-Things (IoT) applications. It will support efficient ways in which the core business logic of these large scale and monolithic systems can be extended into resource control and data sensing functions managed through the IoT. The project will develop a novel, fine-grained software architecture style suitable for loca ....Embedding Enterprise Systems in IoT Fog Networks through Microservices. The project will enable automated re-engineering of enterprise systems, to allow them to reused in Internet-of-Things (IoT) applications. It will support efficient ways in which the core business logic of these large scale and monolithic systems can be extended into resource control and data sensing functions managed through the IoT. The project will develop a novel, fine-grained software architecture style suitable for localised IoT execution, through microservices executing autonomously on nodes of IoT fog networks. It will develop new techniques for automated discovery of microservices from enterprise systems and the verification of future-state system execution based on current-state behavioural and other properties such as security.Read moreRead less
Re-engineering enterprise systems for microservices in the cloud. This project will enable automatic re-engineering of large enterprise applications to run in modern cloud environments as microservices. Microservices are the latest wave of service-based software, capable of exploiting the high performance and third-party integration opportunities made available through the cloud. The project will develop new techniques for analysing enterprise systems code and execution data, and making recommen ....Re-engineering enterprise systems for microservices in the cloud. This project will enable automatic re-engineering of large enterprise applications to run in modern cloud environments as microservices. Microservices are the latest wave of service-based software, capable of exploiting the high performance and third-party integration opportunities made available through the cloud. The project will develop new techniques for analysing enterprise systems code and execution data, and making recommendations for restructuring suitable parts as microservices. These microservices manage individual business objects via sets of lightweight distributed computational operations. The outcomes will support progressive evolution of an enterprise system, into distributed microservices running in public clouds, while still being integrated with "backend" systems.Read moreRead less
Industrial Transformation Research Hubs - Grant ID: IH210100001
Funder
Australian Research Council
Funding Amount
$2,062,428.00
Summary
ARC Research Hub for Nutrients in a Circular Economy (NiCE). Urban utilities are in need to design resilient wastewater infrastructure to tackle the pressures of urban intensification, waterways pollution and climate change. This Hub aims to transform the wastewater industry with an unprecedented, city-scale circular economy of nutrients based on urine separation and processing at building level, to produce safe and effective liquid fertilisers. By engaging with stakeholders across the value cha ....ARC Research Hub for Nutrients in a Circular Economy (NiCE). Urban utilities are in need to design resilient wastewater infrastructure to tackle the pressures of urban intensification, waterways pollution and climate change. This Hub aims to transform the wastewater industry with an unprecedented, city-scale circular economy of nutrients based on urine separation and processing at building level, to produce safe and effective liquid fertilisers. By engaging with stakeholders across the value chain, this Hub expects to bring two urine processing technologies to commercial readiness, and to produce new regulations and business models for the circular economy. This will add resilience to the wastewater and urban farming industries, and will create market opportunities for new Australian technologies.Read moreRead less
Deep Learning Architecture with Context Adaptive Features for Image Parsing. This project aims to develop a novel deep learning network architecture with contextual adaptive features for image parsing that can improve the object detection accuracy in real-world applications. A number of innovative methods for deep learning, contextual features and network parameter selection will be developed and investigated. The impact of the proposed architecture and features will be improved object-detection ....Deep Learning Architecture with Context Adaptive Features for Image Parsing. This project aims to develop a novel deep learning network architecture with contextual adaptive features for image parsing that can improve the object detection accuracy in real-world applications. A number of innovative methods for deep learning, contextual features and network parameter selection will be developed and investigated. The impact of the proposed architecture and features will be improved object-detection accuracy and advances in deep learning network architecture for image parsing. The intended outcomes are deep learning network architecture, contextual feature extraction techniques and network parameter optimisation techniques for image parsing.Read moreRead less
A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extrac ....A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extractor for automatically extracting features, an optimiser for finding optimal parameters and non-iterative ensemble learning technique for classification of features into classes. The impact of this project will be automatic feature extractors and classifiers for real-world applications.Read moreRead less
Mitigating the risks of cyberattacks on cyber-physical power systems. Cyber threats are a pertinent issue facing power systems as part of national critical infrastructure. This project will develop a systematic theory to capture the dynamic risk propagation of cyberattacks on cyber-physical power systems. Focusing on the physical domain of cyber-physical power systems, the theory includes offline risk modelling with consideration of attack intentions for risk propagation of cyberattacks, an onli ....Mitigating the risks of cyberattacks on cyber-physical power systems. Cyber threats are a pertinent issue facing power systems as part of national critical infrastructure. This project will develop a systematic theory to capture the dynamic risk propagation of cyberattacks on cyber-physical power systems. Focusing on the physical domain of cyber-physical power systems, the theory includes offline risk modelling with consideration of attack intentions for risk propagation of cyberattacks, an online risk assessment method to quantify the risk propagation of cyberattacks, and resilient control strategies to mitigate cyberattack risks. The outcomes will not only advance knowledge in cyber-physical security but also facilitate an accelerated adoption of the increasing renewable energy sources into the power grid.Read moreRead less
Statistical Methods for Next Generation Genome-Wide Association Studies. This project aims to develop cutting-edge statistical methods to analyse large genomic datasets and identify genetic variants associated with inter-individual differences in various human traits. Knowledge of trait-associated DNA variants is instrumental in understanding how natural selection has shaped human traits. By integrating genomic data from diverse and underrepresented populations, this project further expects to c ....Statistical Methods for Next Generation Genome-Wide Association Studies. This project aims to develop cutting-edge statistical methods to analyse large genomic datasets and identify genetic variants associated with inter-individual differences in various human traits. Knowledge of trait-associated DNA variants is instrumental in understanding how natural selection has shaped human traits. By integrating genomic data from diverse and underrepresented populations, this project further expects to contribute to the equitable use of genomic technologies in humans, regardless of geographical origins. Expected outcomes of this research include novel analysis methods and software tools, which should broadly and significantly benefit gene discovery in other species, including those of agricultural relevance.Read moreRead less