Deep Learning for Computational Problems in Hardware Security : Modeling Attacks on Strong Physically Unclonable Function Circuits Paperback / softback
by Pranesh Santikellur, Rajat Subhra Chakraborty
Part of the Studies in Computational Intelligence series
Paperback / softback
Description
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.
Information
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Out of stock
- Format:Paperback / softback
- Pages:84 pages, 18 Illustrations, color; 13 Illustrations, black and white; XIII, 84 p. 31 illus., 18 illu
- Publisher:Springer Verlag, Singapore
- Publication Date:17/09/2023
- Category:
- ISBN:9789811940194
Other Formats
- Hardback from £78.79
Information
-
Out of stock
- Format:Paperback / softback
- Pages:84 pages, 18 Illustrations, color; 13 Illustrations, black and white; XIII, 84 p. 31 illus., 18 illu
- Publisher:Springer Verlag, Singapore
- Publication Date:17/09/2023
- Category:
- ISBN:9789811940194
