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Developing smart embedded host-based intrusion detection systems. Computer intrusion is a major concern in many places. It is estimated that cybercrime cost firms US$1 trillion globally in 2008. Many serious cyber attacks, including cyber espionage, do not generate significant network traffic and can easily penetrate network-based intrusion detection systems (NIDS). Such attacks often attempt to compromise individual hosts and hence they are best detected at the host level. We aim to design i ....Developing smart embedded host-based intrusion detection systems. Computer intrusion is a major concern in many places. It is estimated that cybercrime cost firms US$1 trillion globally in 2008. Many serious cyber attacks, including cyber espionage, do not generate significant network traffic and can easily penetrate network-based intrusion detection systems (NIDS). Such attacks often attempt to compromise individual hosts and hence they are best detected at the host level. We aim to design innovative host-based IDS, as a complement to the NIDS, to address this issue. The outcomes of this project will strengthen the national capability to resist attacks by criminals and terrorists on Australian networked critical infrastructures and also enhance the global competitiveness of Australia’s information technology industry.Read moreRead less
Detecting Supervisory Control and Data Access (SCADA) malicious programs to protect Australian critical infrastructure. The security of SCADA systems has enormous impact to our national security and economy because they control and monitor critical infrastructure, like power, gas and water facilities and nuclear power plants, etc. This project aims to investigate the security issues and provide innovative technological solutions to detect and prevent such problems.
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE140100121
Funder
Australian Research Council
Funding Amount
$720,000.00
Summary
Equipment for International Collaboration in Gravitational Wave Detection. Equipment for international collaboration in gravitational wave detection: This project will allow the Australian Consortium for Gravitational Astronomy to install optical equipment at its dedicated research facility, and to install data analysis pipelines on new iVEC Pawsey Centre GPU-enabled supercomputers. The equipment is required for research aimed at stabilising instabilities in the new international gravitational w ....Equipment for International Collaboration in Gravitational Wave Detection. Equipment for international collaboration in gravitational wave detection: This project will allow the Australian Consortium for Gravitational Astronomy to install optical equipment at its dedicated research facility, and to install data analysis pipelines on new iVEC Pawsey Centre GPU-enabled supercomputers. The equipment is required for research aimed at stabilising instabilities in the new international gravitational wave detectors currently being commissioned in the USA and Europe. Real time data from the new detectors will be analysed using innovative new techniques. Scientists across Australia will be able to rapidly localise potential gravitational wave sources to direct robotic telescope observations. This could enable the first detection of gravitational waves.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE130100156
Funder
Australian Research Council
Funding Amount
$210,000.00
Summary
Computational infrastructure for machine learning in computer vision. The many trillions of images stored on computers around the world, including more than 100 billion on Facebook alone, represent exactly the information needed to develop artificial vision. All we need do is extract it. This project will develop the computational infrastructure required to allow Australian researchers to achieve this goal.
A theoretical framework for practical partial fingerprint identification. Fingerprints captured from a crime scene are often partial and poor quality which makes it difficult to identify the criminal suspects from large databases. This project will find mathematical models which can estimate the missing information located in the blank areas of a partial fingerprint and effectively identify it.