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Viac informácií o knihe Deep Learning for Computational Problems in Hardware Security

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Anotácia knihy

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.

Parametre knihy

Zaradenie knihy Knihy po anglicky Technology, engineering, agriculture Electronics & communications engineering Electronics engineering

• Celý názov: Deep Learning for Computational Problems in Hardware Security
• Podnázov: Modeling Attacks on Strong Physically Unclonable Function Circuits
• Autor: Pranesh Santikellur, Rajat Subhra Chakraborty
• Jazyk: Angličtina
• Väzba: Brožovaná
• Počet strán: 84
• EAN: 9789811940194
• ID: 43967588
• Nakladateľ: Springer, Berlin
• Hmotnosť: 149 g
• Rozmery: 235 × 155 mm
• Rok vydania: 2023

Obľúbené z iného súdka

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