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Deep Learning for Computational Problems in Hardware Security : Modeling Attacks on Strong Physically Unclonable Function Circuits
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Deep Learning for Computational Problems in Hardware Security : Modeling Attacks on Strong Physically Unclonable Function Circuits
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Deep Learning for Computational Problems in Hardware Security
Lingua: Inglese
Editore: Springer Verlag, Singapore, SG, 2022
ISBN 10: 9811940169 ISBN 13: 9789811940163
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Deep Learning for Computational Problems in Hardware Security : Modeling Attacks on Strong Physically Unclonable Function Circuits
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Deep Learning for Computational Problems in Hardware Security : Modeling Attacks on Strong Physically Unclonable Function Circuits
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Deep Learning for Computational Problems in Hardware Security
Lingua: Inglese
Editore: Springer Verlag, Singapore, 2022
ISBN 10: 9811940169 ISBN 13: 9789811940163
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Deep Learning for Computational Problems in Hardware Security
Lingua: Inglese
Editore: Springer Verlag, Singapore, 2022
ISBN 10: 9811940169 ISBN 13: 9789811940163
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Deep Learning for Computational Problems in Hardware Security: Modeling Attacks on Strong Physically Unclonable Function Circuits (Studies in Computational Intelligence, 1052)
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Deep Learning for Computational Problems in Hardware Security: Modeling Attacks on Strong Physically Unclonable Function Circuits (Studies in Computational Intelligence, 1052)
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Deep Learning for Computational Problems in Hardware Security (Hardcover)
Lingua: Inglese
Editore: Springer Verlag, Singapore, Singapore, 2022
ISBN 10: 9811940169 ISBN 13: 9789811940163
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Hardcover. Condizione: new. Hardcover. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Deep Learning for Computational Problems in Hardware Security: Modeling Attacks on Strong Physically Unclonable Function Circuits: 1052 (Studies in Computational Intelligence, 1052)
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Deep Learning For Computational Problems
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Deep Learning for Computational Problems in Hardware Security: Modeling Attacks on Strong Physically Unclonable Function Circuits (Studies in Computational Intelligence, 1052)
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Condizione: New. 1st ed. 2023 edition NO-PA16APR2015-KAP.
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Deep Learning for Computational Problems in Hardware Security
Lingua: Inglese
Editore: Springer Verlag, Singapore, SG, 2022
ISBN 10: 9811940169 ISBN 13: 9789811940163
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Aggiungi al carrelloHardback. Condizione: New. 2023 ed. 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.
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Deep Learning For Computational Problems
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Deep Learning for Computational Problems in Hardware Security
Lingua: Inglese
Editore: Springer, Berlin|Springer Nature Singapore|Springer, 2022
ISBN 10: 9811940169 ISBN 13: 9789811940163
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Deep Learning for Computational Problems in Hardware Security: Modeling Attacks on Strong Physically Unclonable Function Circuits
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Deep Learning for Computational Problems in Hardware Security: Modeling Attacks on Strong Physically Unclonable Function Circuits
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Aggiungi al carrelloTaschenbuch. Condizione: 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.
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Aggiungi al carrelloCondizione: 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.
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Aggiungi al carrelloTaschenbuch. Condizione: 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.
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Deep Learning for Computational Problems in Hardware Security (Hardcover)
Lingua: Inglese
Editore: Springer Verlag, Singapore, Singapore, 2022
ISBN 10: 9811940169 ISBN 13: 9789811940163
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Aggiungi al carrelloHardcover. Condizione: new. Hardcover. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Aggiungi al carrelloBuch. Condizione: Neu. Deep Learning for Computational Problems in Hardware Security | Modeling Attacks on Strong Physically Unclonable Function Circuits | Pranesh Santikellur (u. a.) | Buch | xiii | Englisch | 2022 | Springer | EAN 9789811940163 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Deep Learning for Computational Problems in Hardware Security: Modeling Attacks on Strong Physically Unclonable Function Circuits
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Aggiungi al carrelloHardcover. Condizione: Brand New. 97 pages. 9.25x6.10x0.51 inches. In Stock. This item is printed on demand.
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Deep Learning for Computational Problems in Hardware Security
Lingua: Inglese
Editore: Springer, Berlin|Springer Nature Singapore|Springer, 2023
ISBN 10: 9811940193 ISBN 13: 9789811940194
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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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Deep Learning for Computational Problems in Hardware Security: Modeling Attacks on Strong Physically Unclonable Function Circuits (Studies in Computational Intelligence, 1052)
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Deep Learning for Computational Problems in Hardware Security: Modeling Attacks on Strong Physically Unclonable Function Circuits (Studies in Computational Intelligence, 1052)
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Deep Learning for Computational Problems in Hardware Security: Modeling Attacks on Strong Physically Unclonable Function Circuits (Studies in Computational Intelligence)
Da: Biblios, Frankfurt am main, HESSE, Germania
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