RAINBOW - RAdIo Networks Based On machine learning for situation aWareness. This project aims to develop software-defined and cognitive radio networks (SDR) to detect adversarial communications and achieve situation awareness on radio frequency (RF) spectrum. The project will generate novel SDR architectures and new attack-resistant detection algorithms through innovative approaches combining machine learning and game theory. Expected outcomes include a strategic alliance between the University ....RAINBOW - RAdIo Networks Based On machine learning for situation aWareness. This project aims to develop software-defined and cognitive radio networks (SDR) to detect adversarial communications and achieve situation awareness on radio frequency (RF) spectrum. The project will generate novel SDR architectures and new attack-resistant detection algorithms through innovative approaches combining machine learning and game theory. Expected outcomes include a strategic alliance between the University of Melbourne and the Northrop Grumman Corporation. Among significant benefits, the project will improve cybersecurity of RF spectrum as a national asset, help protect critical infrastructure relying on wireless networks such as telecommunications and defence, and build skills in cybersecurity and Artificial Intelligence.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200100863
Funder
Australian Research Council
Funding Amount
$405,398.00
Summary
Privacy Coupling: When Your Personal Devices Betray You. This project aims to propose novel privacy preserving schemes that can protect the privacy of individuals in the era of Internet of things and machine learning. In the recent years, most Australian organizations have been a target of privacy and cybersecurity attacks, affecting their data and network systems. The expected outcomes of this project are privacy preserving schemes that can prevent attackers from compromising the private inform ....Privacy Coupling: When Your Personal Devices Betray You. This project aims to propose novel privacy preserving schemes that can protect the privacy of individuals in the era of Internet of things and machine learning. In the recent years, most Australian organizations have been a target of privacy and cybersecurity attacks, affecting their data and network systems. The expected outcomes of this project are privacy preserving schemes that can prevent attackers from compromising the private information of individuals in IoT and machine learning services, and thus significantly improve the protection against cybersecurity attacks. Significant benefits in social wellbeing and security are expected for all industry, government, and service sectors that collect data about people.Read moreRead less