首页|Findings from University of Technology Sydney Provide New Insights into Machine Learning (A Comprehensive Survey On Poisoning Attacks and Countermeasures In Mac hine Learning)
Findings from University of Technology Sydney Provide New Insights into Machine Learning (A Comprehensive Survey On Poisoning Attacks and Countermeasures In Mac hine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting originating in Ultimo, Aust ralia, by NewsRx journalists, research stated, “The prosperity of machine learni ng has been accompanied by increasing attacks on the training process. Among the m, poisoning attacks have become an emerging threat during model training.” Funders for this research include Australian Research Council, Australian Resear ch Council. The news reporters obtained a quote from the research from the University of Tec hnology Sydney, “Poisoning attacks have profound impacts on the target models, e .g., making them unable to converge or manipulating their prediction results. Mo reover, the rapid development of recent distributed learning frameworks, especia lly federated learning, has further stimulated the development of poisoning atta cks. Defending against poisoning attacks is challenging and urgent. However, the systematic review from a unified perspective remains blank. This survey provide s an in-depth and up-to-date overview of poisoning attacks and corresponding cou nter-measures in both centralized and federated learning. We firstly categorize attack methods based on their goals. Secondly, we offer detailed analysis of the differences and connections among the attack techniques. Furthermore, we presen t countermeasures in different learning framework and highlight their advantages and disadvantages.”
UltimoAustraliaAustralia and New Zea landCyborgsEmerging TechnologiesHealth and MedicineMachine LearningPoi soningUniversity of Technology Sydney
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