Privacy-aware Smart Access Control for Internet-of-Things on Blockchain. This project aims to address privacy and trust issues in Internet-of-Things (IoT) access control mechanism of smart critical infrastructure. This project expects to generate new knowledge in the area of IoT access control by leveraging privacy-preserving techniques, blockchain, and machine learning. Expected outcomes of this project include enhanced capability to build improved techniques for privacy aware tamperproof IoT a ....Privacy-aware Smart Access Control for Internet-of-Things on Blockchain. This project aims to address privacy and trust issues in Internet-of-Things (IoT) access control mechanism of smart critical infrastructure. This project expects to generate new knowledge in the area of IoT access control by leveraging privacy-preserving techniques, blockchain, and machine learning. Expected outcomes of this project include enhanced capability to build improved techniques for privacy aware tamperproof IoT access control with machine learning based anomaly detection. This should provide significant benefits, such as preventing cyber threats on security and privacy of IoT and improving trust in IoT-enabled smart critical infrastructure of Australia.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220100680
Funder
Australian Research Council
Funding Amount
$403,482.00
Summary
Making Anomaly Detection Interpretable & Actionable in Hostile Environments. Anomaly detection plays a vital role in cyber security to identify threat patterns hidden within large volumes of data. However, current approaches experience high false alarm rates in noisy, heterogeneous and adversarial environments. This project aims to identify and interpret anomalies that can disrupt system performance by introducing the concept of actionable anomalies. It will significantly advance the effectivene ....Making Anomaly Detection Interpretable & Actionable in Hostile Environments. Anomaly detection plays a vital role in cyber security to identify threat patterns hidden within large volumes of data. However, current approaches experience high false alarm rates in noisy, heterogeneous and adversarial environments. This project aims to identify and interpret anomalies that can disrupt system performance by introducing the concept of actionable anomalies. It will significantly advance the effectiveness of anomaly detection by developing algorithms that distil local and global structures of data to characterise actionable anomalies and explain their outlying aspects. Project outcomes will enhance the security, trustworthiness and fault-tolerance of critical systems, contributing to international efforts in cyber security.Read moreRead less
Australian Laureate Fellowships - Grant ID: FL190100167
Funder
Australian Research Council
Funding Amount
$2,895,366.00
Summary
The CMOS Quantum Processor: A path to scalable quantum computing. The project aims to develop a quantum computer processor based on a new technology developed by Professor Dzurak in 2014-15. Remarkably, the qubits, or processing elements, utilise the silicon metal-oxide semiconductor field-effect transistors that constitute today’s microprocessor chips, so existing production plants can be used to fast-track development. The project will realise proof-of-principle systems with 10-20 qubits, to r ....The CMOS Quantum Processor: A path to scalable quantum computing. The project aims to develop a quantum computer processor based on a new technology developed by Professor Dzurak in 2014-15. Remarkably, the qubits, or processing elements, utilise the silicon metal-oxide semiconductor field-effect transistors that constitute today’s microprocessor chips, so existing production plants can be used to fast-track development. The project will realise proof-of-principle systems with 10-20 qubits, to resolve critical issues related to readout, error correction, and long-distance on-chip coupling, to take the technology to a commercial-ready stage. Quantum computing is one of the great scientific challenges of this century, with important applications in pharmaceutical design, finance and national security.Read moreRead less
Robust learning of dynamic systems. Robots and other autonomous machines use models of the real world to predict the result of their actions and make decisions, but existing methods used for machine-learning are unreliable in many cases and can be easily fooled. This project aims to make machine-learning of dynamic system models reliable, accurate, and secure. The outcomes of this project will be new models and algorithms that ensure safety and increase accuracy of models learned from data. This ....Robust learning of dynamic systems. Robots and other autonomous machines use models of the real world to predict the result of their actions and make decisions, but existing methods used for machine-learning are unreliable in many cases and can be easily fooled. This project aims to make machine-learning of dynamic system models reliable, accurate, and secure. The outcomes of this project will be new models and algorithms that ensure safety and increase accuracy of models learned from data. This project will benefit robotics, control engineering, infrastructure automation, and other fields that demand the capability to model physical systems from limited data. It will also improve cybersecurity by making learning algorithms resilient to deliberate attacks with false data.Read moreRead less