首页|New Findings from University of Queensland in Machine Learning Provides New Insi ghts (A Configurable Anonymisation Approach for Network Flow Data: Balancing Uti lity and Privacy)
New Findings from University of Queensland in Machine Learning Provides New Insi ghts (A Configurable Anonymisation Approach for Network Flow Data: Balancing Uti lity and Privacy)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Research findings on Machine Learning are discussed in a new report. According tonews reporting originating in Brisba ne, Australia, by NewsRx journalists, research stated, “This paperintroduces a novel anonymisation scheme that enables a configurable trade off between the uti lity of theanonymised network data for Machine Learning (ML)-based Network Intr usion Detection Systems (NIDS)and the level of privacy preservation. The method enhances both the utility and the level of privacyprotection of the anonymised network data containing personal information.”
BrisbaneAustraliaAustralia and New Z ealandCybersecurityCyborgsEmerging TechnologiesMachine LearningUnivers ity of Queensland
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