Springer
Deep Learning for Computational Problems in Hardware Security : Modeling Attacks on Strong Physically Unclonable Function Circuits
Product Code:
9789811940194
ISBN13:
9789811940194
Condition:
New
$117.02
Deep Learning for Computational Problems in Hardware Security : Modeling Attacks on Strong Physically Unclonable Function Circuits
$117.02
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.
| Author: Pranesh Santikellur, Rajat Subhra Chakraborty |
| Publisher: Springer |
| Publication Date: Sep 17, 2023 |
| Number of Pages: NA pages |
| Language: English |
| Binding: Paperback |
| ISBN-10: 9811940193 |
| ISBN-13: 9789811940194 |
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