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
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.
Read moreRead less
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
Data Privacy Protection in Wireless Sensor Networks. This project aims to explore a comprehensive solution for the protection of privacy-sensitive data in wireless sensor networks (WSNs) that are vulnerable to hacking. The project expects to use an innovative approach involving multiple data servers to protect sensor data privacy from data collection to data access and analysis. Expected outcomes of this project include new security and privacy models for WSNs in the setting of multiple servers, ....Data Privacy Protection in Wireless Sensor Networks. This project aims to explore a comprehensive solution for the protection of privacy-sensitive data in wireless sensor networks (WSNs) that are vulnerable to hacking. The project expects to use an innovative approach involving multiple data servers to protect sensor data privacy from data collection to data access and analysis. Expected outcomes of this project include new security and privacy models for WSNs in the setting of multiple servers, new secure protocols, privacy-preserving access control and data analysis protocols, and a prototype of a privacy-preserving WSN system. This should provide significant benefits, such as improved security of sensitive data in the healthcare system, military, utilities and telecommunications.
Read moreRead less
Privacy-Preserving Collaborative Analytics on Sensitive Data. This project aims to develop efficient solutions that allow multiple institutes to carry out collaborative analytics on aggregated data without revealing their sensitive data to each other. The project expects to remedy acute privacy concerns when institutes share sensitive data across boundaries for collective insights. The expected outcomes include a hybrid trust model with distributed trusts to provide malicious security guarantees ....Privacy-Preserving Collaborative Analytics on Sensitive Data. This project aims to develop efficient solutions that allow multiple institutes to carry out collaborative analytics on aggregated data without revealing their sensitive data to each other. The project expects to remedy acute privacy concerns when institutes share sensitive data across boundaries for collective insights. The expected outcomes include a hybrid trust model with distributed trusts to provide malicious security guarantees, lightweight privacy-enhancing techniques to express rich analytical functionalities, and a system platform for real-world applications. This should provide significant benefits such as facilitating industries to safeguard their customers' data and uplift their businesses in a secure and trustworthy fashion.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