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Titlebook: Smart Card Research and Advanced Applications; 17th International C Begül Bilgin,Jean-Bernard Fischer Conference proceedings 2019 Springer

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书目名称Smart Card Research and Advanced Applications
副标题17th International C
编辑Begül Bilgin,Jean-Bernard Fischer
视频videohttp://file.papertrans.cn/869/868619/868619.mp4
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Smart Card Research and Advanced Applications; 17th International C Begül Bilgin,Jean-Bernard Fischer Conference proceedings 2019 Springer
描述This book constitutes the thoroughly refereed post-conference proceedings of the 17th International Conference on Smart Card Research and Advanced Applications, CARDIS 2018, held in Monpellier, France, in November 2018..The 13 revised full papers presented in this book were carefully reviewed and selected from 28 submissions...CARDIS has provided a space for security experts from industry and academia to exchange on security of smart cards and related applications..
出版日期Conference proceedings 2019
关键词artificial intelligence; authentication; backpropagation; computer architecture; cryptography; data secur
版次1
doihttps://doi.org/10.1007/978-3-030-15462-2
isbn_softcover978-3-030-15461-5
isbn_ebook978-3-030-15462-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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A Systematic Study of the Impact of Graphical Models on Inference-Based Attacks on AES,alance) best choice is to utilise an acyclic graph in an independent graph combination setting, which gives us provable convergence to the correct key distribution. We provide evidence using both extensive simulations and a final confirmatory analysis on real trace data.
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Improving Side-Channel Analysis Through Semi-supervised Learning,attack (TA) and its pooled version (TA.). More specifically, for low noise scenario, the results for machine learning techniques and TA are often improved when only a small number of measurements is available in the profiling phase, while there is no significant difference in scenarios where the sup
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Jitter Estimation with High Accuracy for Oscillator-Based TRNGs,. Furthermore, for the first time, we give a theoretical error bound for our jitter estimation. The error bound confirms the . error level of our method. As a consequence, our method will significantly help to evaluate the entropy of RO-based TRNGs accurately. Finally, we present the application of
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n in the tensors. Finally, we combine different machine learning methods with our tensorial representation for improving results in Face recognition and Age estimation. In this scope, face recognition is addressed as a classification problem using Kernel Discriminative Vectors to improve recognition
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