Towards a block-cipher circuit resistant to multiple side channel attacks. This project aims to design, implement and verify the first ever fully comprehensive countermeasure against simultaneous attacks on devices performing a cryptographic task, such as a smart card or a mobile phone used for an electronic payment, through a variety of side channels. Side channel attacks aim to obtain unauthorized access to secret cryptographic keys used by such devices, by collecting and statistically process ....Towards a block-cipher circuit resistant to multiple side channel attacks. This project aims to design, implement and verify the first ever fully comprehensive countermeasure against simultaneous attacks on devices performing a cryptographic task, such as a smart card or a mobile phone used for an electronic payment, through a variety of side channels. Side channel attacks aim to obtain unauthorized access to secret cryptographic keys used by such devices, by collecting and statistically processing measurements of by-products of the physical operation of these devices, such as the consumed power or electromagnetic radiation. Rapid proliferation of personal devices used for electronic payments or containing sensitive data makes such a comprehensive countermeasure urgently needed.Read moreRead less
Design automation for secure, reliable and energy efficient embedded processors. This project seeks to create a methodology to design and generate processors which are both secure, reliable and energy efficient for deployment in Internet of Things (IoT) systems, which require little on-going maintenance. In such systems, both security and reliability are paramount, particularly in medical devices, control devices in critical machinery, financial transactions and automotive electronics. The proje ....Design automation for secure, reliable and energy efficient embedded processors. This project seeks to create a methodology to design and generate processors which are both secure, reliable and energy efficient for deployment in Internet of Things (IoT) systems, which require little on-going maintenance. In such systems, both security and reliability are paramount, particularly in medical devices, control devices in critical machinery, financial transactions and automotive electronics. The project will use an open RISC-V processor which is sufficiently flexible to function as a base processor, with a myriad of tools such as compilers and debuggers available. Reliable computing machinery will enable systems to work in hostile environments and be functionally correct for longer.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
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?