9789811940194 - Deep Learning for Computational Problems in Hardware Security: Modeling Attacks on Strong Physically Unclonable Function Circuits: 1052 (Studies in Computational In... (9 resultados)

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Paperback. Condición: Brand New. 97 pages. 9.25x6.10x0.21 inches. In Stock.

Taschenbuch. Condición: 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.

Taschenbuch. Condición: 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.

Condición: new. Questo è un articolo print on demand.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 100 pp. Englisch.

Condición: 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.

Condición: New. PRINT ON DEMAND pp. 100.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 100 pp. Englisch.