Preventing Exfiltration of Sensitive Data by Malicious Insiders or Malwares. Data exfiltration is a serious threat as highlighted in recent leakage of sensitive data that resulted in huge economic losses as well as unprecedented breaches of national security. The aim of this project is to develop a comprehensive and robust solution for detection and prevention of sensitive data exfiltration attempts by malware and unauthorised human users. Expected outcomes include scalable monitoring methods an ....Preventing Exfiltration of Sensitive Data by Malicious Insiders or Malwares. Data exfiltration is a serious threat as highlighted in recent leakage of sensitive data that resulted in huge economic losses as well as unprecedented breaches of national security. The aim of this project is to develop a comprehensive and robust solution for detection and prevention of sensitive data exfiltration attempts by malware and unauthorised human users. Expected outcomes include scalable monitoring methods and efficient algorithms that will be able to prevent real-time exfiltration and identify previously undetected exfiltration of sensitive data. This should provide significant benefits to governments, defence networks as well as businesses and health sectors, as it will protect them from sophisticated cyber attacks.
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Industrial Transformation Research Hubs - Grant ID: IH210100040
Funder
Australian Research Council
Funding Amount
$5,000,000.00
Summary
ARC RESEARCH HUB FOR CONNECTED SENSORS FOR HEALTH. This Hub aims to develop, manufacture and deploy high-tech, cyber-secure, medically-certified IoT sensors to global health markets by integrating disparate Australian capabilities into a productive end-to-end value chain. This Hub expects to position Australia at the forefront of connected health by integrating sensor science with cyber-secure data analytics, regulatory approval and certified manufacturing capabilities. Expected outcomes of this ....ARC RESEARCH HUB FOR CONNECTED SENSORS FOR HEALTH. This Hub aims to develop, manufacture and deploy high-tech, cyber-secure, medically-certified IoT sensors to global health markets by integrating disparate Australian capabilities into a productive end-to-end value chain. This Hub expects to position Australia at the forefront of connected health by integrating sensor science with cyber-secure data analytics, regulatory approval and certified manufacturing capabilities. Expected outcomes of this Hub include advanced manufacturing capacity for connected sensors, strategic partnerships and commercialisation skills to translate sensors research to create economic benefits such as jobs and locally-made products for domestic and export markets, as well as improving the health of Australians.Read moreRead less
Gelled electrolyte materials for toxic gas sensing. This project aims to develop and implement an alternative approach to the current methods of monitoring of oxygen and toxic gas levels. The aim is to use novel gelled electrolytes based on ionic liquids and polymers, combined with miniaturised sensor devices, to create a robust membrane-free and spill-less design. Amperometric gas sensors are commonly employed to monitor oxygen and toxic gas levels, but the technology used is still based on a ....Gelled electrolyte materials for toxic gas sensing. This project aims to develop and implement an alternative approach to the current methods of monitoring of oxygen and toxic gas levels. The aim is to use novel gelled electrolytes based on ionic liquids and polymers, combined with miniaturised sensor devices, to create a robust membrane-free and spill-less design. Amperometric gas sensors are commonly employed to monitor oxygen and toxic gas levels, but the technology used is still based on a 1950s design. The expected outcome of the project is to make fundamental advances in the design of materials that are not affected by humidity changes and which impart selectivity towards particular gases. This will provide the basis for a new generation of low-cost, miniaturised, selective sensors for use in applications such as wearable toxic gas sensors, and as leak detectors on hydrogen-powered vehicles.Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC210100056
Funder
Australian Research Council
Funding Amount
$3,975,864.00
Summary
ARC Training Centre for Next-Gen Technologies in Biomedical Analysis . The Centre for Next-Gen Technologies in Biomedical Analysis will deliver workforce trained in the development of transformative technologies that will rapidly expand the Australian pharmaceutical, diagnostic and defence sector. The university-industry partnership will increase Australia’s manufacturing capability by fast tracking screening, by integrating 3D printing, advanced sensing, big data analytics, machine learning an ....ARC Training Centre for Next-Gen Technologies in Biomedical Analysis . The Centre for Next-Gen Technologies in Biomedical Analysis will deliver workforce trained in the development of transformative technologies that will rapidly expand the Australian pharmaceutical, diagnostic and defence sector. The university-industry partnership will increase Australia’s manufacturing capability by fast tracking screening, by integrating 3D printing, advanced sensing, big data analytics, machine learning and artificial intelligence for the delivery of optimal solutions in diagnosis, treatment and wellbeing. The centre will deliver training in Industry 4.0 skills which will boost early-stage scale-up and accelerate the sector’s supply chain, which is pivotal for the Australian industries to maintain a competitive edge. Read moreRead less
Secure Management of Internet of Things Data for Critical Surveillance. This project aims to develop innovative models/algorithms to manage Internet of Things (IoT) data safely and reliably. This project expects to generate new knowledge in the area of classified information governance using innovative data collection, transmission and analysis techniques that overcome the security concerns in large-scale collaborative sensing. Expected outcomes include novel abstract interfaces for IoT, adaptiv ....Secure Management of Internet of Things Data for Critical Surveillance. This project aims to develop innovative models/algorithms to manage Internet of Things (IoT) data safely and reliably. This project expects to generate new knowledge in the area of classified information governance using innovative data collection, transmission and analysis techniques that overcome the security concerns in large-scale collaborative sensing. Expected outcomes include novel abstract interfaces for IoT, adaptive trust and integrity preserving methods, and reliable distributed data processing mechanisms to mitigate vulnerabilities in real-time IoT-enabled critical surveillance. This should provide significant benefits to Australia's economy, one of which is the enhanced consumer-centric adoption of IoT for sensitive operations.Read moreRead less
Personalised Privacy-Preserving Network Data Publishing System . Data sharing has become a driving force for many businesses in industrial sectors. This project aims to develop a privacy preserving network data publishing system that can preserve user privacy in a personalised way while maintaining maximal utility of the published data. To make accurate privacy preservation, this project will design novel learning models to derive accurate users’ correlation and their privacy intention, develop ....Personalised Privacy-Preserving Network Data Publishing System . Data sharing has become a driving force for many businesses in industrial sectors. This project aims to develop a privacy preserving network data publishing system that can preserve user privacy in a personalised way while maintaining maximal utility of the published data. To make accurate privacy preservation, this project will design novel learning models to derive accurate users’ correlation and their privacy intention, develop efficient privacy preserving algorithms to deal with static and dynamic network data sharing. The success of this project will benefit many industries and government agencies to reduce users’ privacy breaches, avoid illegal consequences of sharing data, and enhance these service providers’ service quality.Read moreRead less
Fine-grained Human Action Recognition with Deep Graph Neural Networks. This project aims to develop novel graph neural network based deep learning algorithms for fine-grained human action recognition. This project expects to bring human action analysis to the next level and to significantly advance the analysis of subtle yet complex human actions. Expected outcomes of this project include theoretical advances on graph representation based deep learning algorithms for spatial-temporal data, and e ....Fine-grained Human Action Recognition with Deep Graph Neural Networks. This project aims to develop novel graph neural network based deep learning algorithms for fine-grained human action recognition. This project expects to bring human action analysis to the next level and to significantly advance the analysis of subtle yet complex human actions. Expected outcomes of this project include theoretical advances on graph representation based deep learning algorithms for spatial-temporal data, and enabling techniques for more objective human action analysis in many domains such as sports and health. This should provide significant benefits to any application domain involving big and complex spatial-temporal data for finer analytics and better knowledge discovery.Read moreRead less
Multi-Modal Dictionary Learning for Smart City Operation and Management. This Project aims to provide new digital asset management tools for city councils to improve city services by utilising new sensing and automated learning technologies for recognising, tracking and auditing of assets. Currently, there are no digital tools available to handle these services. This project proposes new multi-modal sensing and mapping of city asset techniques by building new multi-modal dictionary learning proc ....Multi-Modal Dictionary Learning for Smart City Operation and Management. This Project aims to provide new digital asset management tools for city councils to improve city services by utilising new sensing and automated learning technologies for recognising, tracking and auditing of assets. Currently, there are no digital tools available to handle these services. This project proposes new multi-modal sensing and mapping of city asset techniques by building new multi-modal dictionary learning procedures. The new framework will recognise different conditions of city assets in real-time to make decisions. Expected outcomes of this Project include integration and easy access of assets with unique digital identities to help city councils, governments, and navigation services for real-time asset monitoring.Read moreRead less
Personalised Online Learning Analytics by Exploring Multilayer Graph Data . Learning analytics is becoming a significant factor in reducing the drop-out rate of students in online learning. The aim of this project is to develop a reliable, robust, real-time analysis system that automatically reveals multilayered relationships, evaluates students' learning performance, and generates a personal study plan through discovery. This project includes the design of novel algorithms for multilayer graph ....Personalised Online Learning Analytics by Exploring Multilayer Graph Data . Learning analytics is becoming a significant factor in reducing the drop-out rate of students in online learning. The aim of this project is to develop a reliable, robust, real-time analysis system that automatically reveals multilayered relationships, evaluates students' learning performance, and generates a personal study plan through discovery. This project includes the design of novel algorithms for multilayer graph processing, pattern recognition in learning activities, learning performance assessment, and personalised study plan recommendations. The success of this project will significantly enhance the success of online education both in Australia and worldwide and; hence, will save time, money and resources for end users.Read moreRead less