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Research Topic : NEURAL NETWORK
Socio-Economic Objective : Information processing services
Australian State/Territory : ACT
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  • Funded Activity

    Discovery Projects - Grant ID: DP0346541

    Funder
    Australian Research Council
    Funding Amount
    $167,213.00
    Summary
    Network Intrusion Detection via Machine Learning. Computer security is an increasingly important, yet complex task. It takes significant skills to configure systems properly such that they are safe from malicious attacks. The proposed project aims at designing automatic systems which are able to adapt to an existing network configuration and which detect novel and unusual events. For this purpose we will use modern machine learning techniques, mainly based on kernels. In particular, rec .... Network Intrusion Detection via Machine Learning. Computer security is an increasingly important, yet complex task. It takes significant skills to configure systems properly such that they are safe from malicious attacks. The proposed project aims at designing automatic systems which are able to adapt to an existing network configuration and which detect novel and unusual events. For this purpose we will use modern machine learning techniques, mainly based on kernels. In particular, recently developed algorithms to estimate the support of a distribution and detect rare events will be employed in this context. The project is in cooperation with Dr. Ralf Herbrich (Microsoft Research, Cambridge).
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    Funded Activity

    Linkage Projects - Grant ID: LP0668998

    Funder
    Australian Research Council
    Funding Amount
    $171,000.00
    Summary
    Handling unreliable, uncertain and inadequate data for Intelligence led Investigation. Intelligence led investigation has been successful recently in drug and people smuggling, preparation or instigation of acts of terrorism, and can benefit profoundly from the techniques we will develop, in the timely management and inference from many sources and kinds of uncertain information. This work will assist in making Australia a safer and more secure country. E.g., Australian Bureau of Statistics .... Handling unreliable, uncertain and inadequate data for Intelligence led Investigation. Intelligence led investigation has been successful recently in drug and people smuggling, preparation or instigation of acts of terrorism, and can benefit profoundly from the techniques we will develop, in the timely management and inference from many sources and kinds of uncertain information. This work will assist in making Australia a safer and more secure country. E.g., Australian Bureau of Statistics figures show that for 2004, investigations of some 35% of murders, 63% of kidnappings, and 80% of robberies are incomplete at 30 days. Terrorism investigations are harder in that usually there is no initial crime trigger for an investigation. Any assistance our tools can provide in will be of significant benefit to Australia.
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    Funded Activity

    Discovery Projects - Grant ID: DP0208969

    Funder
    Australian Research Council
    Funding Amount
    $258,752.00
    Summary
    Kernel and Margin Based Machine Learning Algorithms. Certain machine learning algorithms, such as support vector machines, utilizing the ideas of large margins and kernels have attracted much attention lately because of their impressive performance on real world problems such as optical character recognition. We plan to refine and extend such algorithms to a wide range of different machine learning problems such as gene sequence analysis, image processing and text classification. Expected .... Kernel and Margin Based Machine Learning Algorithms. Certain machine learning algorithms, such as support vector machines, utilizing the ideas of large margins and kernels have attracted much attention lately because of their impressive performance on real world problems such as optical character recognition. We plan to refine and extend such algorithms to a wide range of different machine learning problems such as gene sequence analysis, image processing and text classification. Expected outcomes include the development of software that allows the solution of hitherto unsolved machine learning problems, and the ability to solve problems larger than those solvable by the current generation of machine learning tools.
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