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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Efficient and secure data integrity auditing on cloud. Data auditing presents a promising way for verifying user data integrity on cloud, i.e., whether user privacy sensitive data such as identity information on cloud is modified or lost. Current auditing approaches lack sufficient efficiency and security. This results in that they cannot provide timely warning and precaution on potential data loss threats. This project aims to systematically investigate this significant challenge and expects to ....Efficient and secure data integrity auditing on cloud. Data auditing presents a promising way for verifying user data integrity on cloud, i.e., whether user privacy sensitive data such as identity information on cloud is modified or lost. Current auditing approaches lack sufficient efficiency and security. This results in that they cannot provide timely warning and precaution on potential data loss threats. This project aims to systematically investigate this significant challenge and expects to establish innovative research and solutions for enabling efficient and secure data integrity auditing on cloud. The project outcomes will help to safeguard Australian community in fast-growing cyber world, and benefit to fast-growing user privacy sensitive data hosting and applications on cloud.Read moreRead less
Attribution of Machine-generated Code for Accountability. Machine-generated (or neural) code is usually produced by AI tools to speed up software development. However, such codes have recently raised serious security and privacy concerns. This project aims to attribute these codes to their generative models for accountability purposes. In the process, a series of new techniques are developed to differentiate between the codes generated by different models. The outcomes include analysis of neural ....Attribution of Machine-generated Code for Accountability. Machine-generated (or neural) code is usually produced by AI tools to speed up software development. However, such codes have recently raised serious security and privacy concerns. This project aims to attribute these codes to their generative models for accountability purposes. In the process, a series of new techniques are developed to differentiate between the codes generated by different models. The outcomes include analysis of neural code fingerprints, classification of neural codes, and theories to verify the correctness of code attribution. These will provide significant benefits, ranging from copyright protection to privacy preservation. This project is timely since currently the software community is pervasively using neural codes.Read moreRead less
Knowledge Graph-driven Software Vulnerability Risk Discovery and Assessment. This project aims to alleviate cyberattacks which are increasingly being crafted to attack software vulnerabilities and weaknesses by utilising advanced knowledge graphs and deep learning techniques. This project expects to construct an innovative software vulnerability knowledge graph and develop advanced graph-based algorithms and models. Expected outcomes of this project include the enhanced capacity to defend agains ....Knowledge Graph-driven Software Vulnerability Risk Discovery and Assessment. This project aims to alleviate cyberattacks which are increasingly being crafted to attack software vulnerabilities and weaknesses by utilising advanced knowledge graphs and deep learning techniques. This project expects to construct an innovative software vulnerability knowledge graph and develop advanced graph-based algorithms and models. Expected outcomes of this project include the enhanced capacity to defend against cyberattacks for both organisations and individuals in Australia and beyond, theory development in graph theory, refined graph neural network models and improved graph transfer learning algorithms.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
Advanced Machine Learning with Bilevel Optimization. There is an urgent need to develop a new machine learning (ML) paradigm that can overcome data-privacy and model-size constraints in real-world applications. This project aims to develop an advanced paradigm of ML with bilevel optimisation, called bilevel ML. A theoretically-guaranteed fast approximate solver and a new fuzzy bilevel learning framework will be developed to achieve the aim in complex situations; a methodology to transfer knowled ....Advanced Machine Learning with Bilevel Optimization. There is an urgent need to develop a new machine learning (ML) paradigm that can overcome data-privacy and model-size constraints in real-world applications. This project aims to develop an advanced paradigm of ML with bilevel optimisation, called bilevel ML. A theoretically-guaranteed fast approximate solver and a new fuzzy bilevel learning framework will be developed to achieve the aim in complex situations; a methodology to transfer knowledge and an approach to fast-adapt bilevel optimization solutions when required computing resources change. The anticipated outcomes should significantly improve the reliability of ML with benefits for safety learning and computing resource optimisation in ML-based data analytics.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240101089
Funder
Australian Research Council
Funding Amount
$436,847.00
Summary
Trustworthy Hypothesis Transfer Learning. It is urgent to develop a new hypothesis transfer learning scheme that can overcome potential risks when finetuning unreliable large-scale pre-trained models. This project aims to develop an advanced and reliable scheme of hypothesis transfer learning, called Trustworthy Hypothesis Transfer Learning (TrustHTL). A new theoretically guaranteed heterogeneous hypothesis transfer learning framework will be developed to handle heterogeneous situations; a metho ....Trustworthy Hypothesis Transfer Learning. It is urgent to develop a new hypothesis transfer learning scheme that can overcome potential risks when finetuning unreliable large-scale pre-trained models. This project aims to develop an advanced and reliable scheme of hypothesis transfer learning, called Trustworthy Hypothesis Transfer Learning (TrustHTL). A new theoretically guaranteed heterogeneous hypothesis transfer learning framework will be developed to handle heterogeneous situations; a methodology to disinherit risks of pre-trained models and a new fuzzy relation based distributional discrepancy in heterogeneous transfer learning scenarios. The outcomes should significantly improve the reliability of machine learning with benefits for safety learning in data analytics.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE230100473
Funder
Australian Research Council
Funding Amount
$410,154.00
Summary
Effective integration of human and automated analyses for security testing. This DECRA project aims to significantly improve the performance of current state-of-the-art automated security testing approaches, enabling them to discover more security bugs in strict time constraints. The key innovation of the project is its novel way to embrace human element to leverage the ingenuity of the developers. This project will help companies improve the security and reliability of their products, thwarting ....Effective integration of human and automated analyses for security testing. This DECRA project aims to significantly improve the performance of current state-of-the-art automated security testing approaches, enabling them to discover more security bugs in strict time constraints. The key innovation of the project is its novel way to embrace human element to leverage the ingenuity of the developers. This project will help companies improve the security and reliability of their products, thwarting cyberattacks that cost Australian business $29 billion each year. The knowledge from this project will be transferred and integrated into higher education subjects to train the next generations of software developers, who are responsible to build security-critical systems that we all rely on now and in the future.Read moreRead less
Exploiting Geometries of Learning for Fast, Adaptive and Robust AI. This project aims to uniquely exploit geometric manifolds in deep learning to advance the frontier of Artificial Intelligence (AI) research and applications in cybersecurity and general cognitive tasks. It expects to develop new theories, algorithms, tools, and technologies for machine learning systems that are fast, adaptive, lifelong and robust, even with limited supervision. Expected outcomes will enhance Australia's capabili ....Exploiting Geometries of Learning for Fast, Adaptive and Robust AI. This project aims to uniquely exploit geometric manifolds in deep learning to advance the frontier of Artificial Intelligence (AI) research and applications in cybersecurity and general cognitive tasks. It expects to develop new theories, algorithms, tools, and technologies for machine learning systems that are fast, adaptive, lifelong and robust, even with limited supervision. Expected outcomes will enhance Australia's capability and competitiveness in AI, and deliver robust and trustworthy learning technology. The project should provide significant benefits not only in advancing scientific and translational knowledge but also in accelerating AI innovations, safeguarding cyberspace, and reducing the burden on defence expenses in Australia.Read moreRead less