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Zustand: New.
Sprache: Englisch
Verlag: Springer Nature Singapore, 2022
ISBN 10: 9811940169 ISBN 13: 9789811940163
Anbieter: Buchpark, Trebbin, Deutschland
Zustand: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | The book discusses a broad overview of traditional machine learning methods and state-of-the-art deep learning practices for hardware security applications, in particular the techniques of launching potent "modeling attacks" on Physically Unclonable Function (PUF) circuits, which are promising hardware security primitives. The volume is self-contained and includes a comprehensive background on PUF circuits, and the necessary mathematical foundation of traditional and advanced machine learning techniques such as support vector machines, logistic regression, neural networks, and deep learning. This book can be used as a self-learning resource for researchers and practitioners of hardware security, and will also be suitable for graduate-level courses on hardware security and application of machine learning in hardware security. A stand-out feature of the book is the availability of reference software code and datasets to replicate the experiments described in the book.
Sprache: Englisch
Verlag: Springer, Berlin|Springer Nature Singapore|Springer, 2022
ISBN 10: 9811940169 ISBN 13: 9789811940163
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Gebunden. Zustand: New. The book discusses a broad overview of traditional machine learning methods and state-of-the-art deep learning practices for hardware security applications, in particular the techniques of launching potent modeling attacks on Physically Unclonable Functio.
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Taschenbuch. Zustand: Neu. Deep Learning for Computational Problems in Hardware Security | Modeling Attacks on Strong Physically Unclonable Function Circuits | Pranesh Santikellur (u. a.) | Taschenbuch | xiii | Englisch | 2023 | Springer | EAN 9789811940194 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer, Springer Nature Singapore, 2023
ISBN 10: 9811940193 ISBN 13: 9789811940194
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The book discusses a broad overview of traditional machine learning methods and state-of-the-art deep learning practices for hardware security applications, in particular the techniques of launching potent 'modeling attacks' on Physically Unclonable Function (PUF) circuits, which are promising hardware security primitives. The volume is self-contained and includes a comprehensive background on PUF circuits, and the necessary mathematical foundation of traditional and advanced machine learning techniques such as support vector machines, logistic regression, neural networks, and deep learning. This book can be used as a self-learning resource for researchers and practitioners of hardware security, and will also be suitable for graduate-level courses on hardware security and application of machine learning in hardware security. A stand-out feature of the book is the availability of reference software code and datasets to replicate the experiments described in the book.
Sprache: Englisch
Verlag: Springer, Springer Nature Singapore, 2022
ISBN 10: 9811940169 ISBN 13: 9789811940163
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The book discusses a broad overview of traditional machine learning methods and state-of-the-art deep learning practices for hardware security applications, in particular the techniques of launching potent 'modeling attacks' on Physically Unclonable Function (PUF) circuits, which are promising hardware security primitives. The volume is self-contained and includes a comprehensive background on PUF circuits, and the necessary mathematical foundation of traditional and advanced machine learning techniques such as support vector machines, logistic regression, neural networks, and deep learning. This book can be used as a self-learning resource for researchers and practitioners of hardware security, and will also be suitable for graduate-level courses on hardware security and application of machine learning in hardware security. A stand-out feature of the book is the availability of reference software code and datasets to replicate the experiments described in the book.