Crowd tracking and visual analytics for rapidly deployable imaging devices. Crowd tracking and visual analytics for rapidly deployable imaging devices. This project aims to develop visual analytics technology that adds machine intelligence to a rapidly deployable time-lapse imaging platform. Such devices can operate on solar and wind power, and be remotely programmed (via a cellular network) to take photos and send them to a server at given times. This project, which focuses on monitoring crowds ....Crowd tracking and visual analytics for rapidly deployable imaging devices. Crowd tracking and visual analytics for rapidly deployable imaging devices. This project aims to develop visual analytics technology that adds machine intelligence to a rapidly deployable time-lapse imaging platform. Such devices can operate on solar and wind power, and be remotely programmed (via a cellular network) to take photos and send them to a server at given times. This project, which focuses on monitoring crowds of objects of interest, is expected to introduce “smart” imaging platforms that could be triggered and shoot high-quality photographs when “events of interest” occur. This project could make Australia both a world leader in video analytics and secure through on-line threat detection, and improve traffic control and agriculture.Read moreRead less
A fast and effective automated insider threat detection and prediction system. Threats from insiders directly compromises the security, privacy and integrity of Australian e-commerce, large databases and communication channels. This project will provide an essential step in combating this criminal activity by developing methods to detect such threats and secure the public's information against exposure and identity theft.
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?
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
Secure user authentication with continuous adaptive risk evaluation. Users typically authenticate to any given system only once - when they first access it (for example, through providing a password or fingerprint). The prevalence of single sign-on further allows this single authentication to be sufficient for access to multiple systems. Thus an adversary can obtain a large degree of access from stealing a single password, hijacking a user's session, or even simply borrowing their phone. This pr ....Secure user authentication with continuous adaptive risk evaluation. Users typically authenticate to any given system only once - when they first access it (for example, through providing a password or fingerprint). The prevalence of single sign-on further allows this single authentication to be sufficient for access to multiple systems. Thus an adversary can obtain a large degree of access from stealing a single password, hijacking a user's session, or even simply borrowing their phone. This project aims to develop a continuous authentication approach based on user behaviour - typical interactions plus biometrics (for example, keystroke dynamics) - combined with a risk adaptive assessment of the resources being accessed, resulting in re-authentication requests in the event of a suspected compromise.Read moreRead less
In for the count: Maximising trust and reliability in Australian elections. This project aims to develop innovative approaches to identifying, measuring, and evaluating errors and purposeful intervention in the uniquely complex elections at the basis of Australian democracy. Such methods can underpin a world-class election auditing system, which contends with the risks that are emerging at the intersection of election digitisation, cybersecurity and foreign interference. The project’s expected o ....In for the count: Maximising trust and reliability in Australian elections. This project aims to develop innovative approaches to identifying, measuring, and evaluating errors and purposeful intervention in the uniquely complex elections at the basis of Australian democracy. Such methods can underpin a world-class election auditing system, which contends with the risks that are emerging at the intersection of election digitisation, cybersecurity and foreign interference. The project’s expected outcomes are new auditing methods, tested on real Australian election data, with their benefits quantified against global best practice. The research outputs should help reinforce the community’s trust in Australian elections, which are a foundation for our security, social cohesion, and political resilience.Read moreRead less
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: 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: 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