Discovery Early Career Researcher Award - Grant ID: DE170100361
Funder
Australian Research Council
Funding Amount
$360,000.00
Summary
Towards reliable and robust machine learning systems. This project aims to protect machine learning systems from adversarial manipulation. Machine learning technologies are used in e-commerce, search, virtual assistants and self-driving cars. However, they are vulnerable to adversarial manipulations which are imperceptible to humans but can cause systems to fail, thereby undermining their usefulness or possibly causing disasters. Less vulnerable machine learning systems are expected to make futu ....Towards reliable and robust machine learning systems. This project aims to protect machine learning systems from adversarial manipulation. Machine learning technologies are used in e-commerce, search, virtual assistants and self-driving cars. However, they are vulnerable to adversarial manipulations which are imperceptible to humans but can cause systems to fail, thereby undermining their usefulness or possibly causing disasters. Less vulnerable machine learning systems are expected to make future autonomous systems, such as self-driving cars and autonomous robots, safer. This project will provide a deeper understanding of how machine learning systems can be made less vulnerable, thereby increasing the safety of future autonomous systems such as self-driving cars and autonomous robots.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE190100046
Funder
Australian Research Council
Funding Amount
$387,000.00
Summary
Fortifying our digital economy: advanced automated vulnerability discovery. This project aims to enable security researchers to detect critical vulnerabilities in large software systems with maximal efficiency, cost-effectively, and with known statistical accuracy. The aim is to develop advanced high-performance fuzzers that effectively thwart malware attacks, ransomware epidemics, and cyber terrorism by exposing security flaws before they can commence. The project will employ a well-established ....Fortifying our digital economy: advanced automated vulnerability discovery. This project aims to enable security researchers to detect critical vulnerabilities in large software systems with maximal efficiency, cost-effectively, and with known statistical accuracy. The aim is to develop advanced high-performance fuzzers that effectively thwart malware attacks, ransomware epidemics, and cyber terrorism by exposing security flaws before they can commence. The project will employ a well-established statistical framework utilised in ecology research to provide fundamental insights to boosting the efficiency of software vulnerability discovery, and on the trade-off between investing more resources and gaining better cyber security guarantees. As our reliance on new technologies is ever growing, this project equips Australia to curb cyber crime cost-effectively.Read moreRead less
View-based processing of pattern matching queries in large graphs. Graph data exist ubiquitously in modern information systems. Graph pattern matching (GPM) finds parts of the data graph that match a given pattern. It has applications in many areas including knowledge discovery, public health, and crime detection. This project will develop novel techniques for the efficient processing of GPM queries in large graphs.
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
Discovery Early Career Researcher Award - Grant ID: DE160100584
Funder
Australian Research Council
Funding Amount
$370,000.00
Summary
Secure and Private Machine Learning. This project intends to answer the question: How can machines learn from data when participants behave maliciously for personal gain? Machine learning and statistics are used in many technologies where participants have an incentive to game the system (eg internet ad placement, e-commerce rating systems, credit risk in finance, health analytics and smart utility grids). However, little is known about how well state-of-the-art statistical inference techniques ....Secure and Private Machine Learning. This project intends to answer the question: How can machines learn from data when participants behave maliciously for personal gain? Machine learning and statistics are used in many technologies where participants have an incentive to game the system (eg internet ad placement, e-commerce rating systems, credit risk in finance, health analytics and smart utility grids). However, little is known about how well state-of-the-art statistical inference techniques fare when data is manipulated by a malicious participant. The project's outcomes aim to ensure that statistical analysis is accurate while preserving data privacy, providing theoretical foundations of secure machine learning in adversarial domains. Potential applications range from cybersecurity defences to measures for balancing security and privacy interests.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
Solid-state quantum communication technology. This project will develop the quantum information devices required to create a quantum communication network for the ultra-secure transmission of data. The key technological challenge is to entangle the quantum state of two crystals separated by kilometres, and maintain this entanglement for many seconds.
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
Machine learning in adversarial environments. Machine learning underpins the technologies driving the economies of both Silicon Valley and Wall Street, from web search and ad placement, to stock predictions and efforts in fighting cybercrime. This project aims to answer the question: How can machines learn from data when contributors act maliciously for personal gain?