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.
| ISBN: | 9789811940163 |
| Publication date: | 16th September 2022 |
| Author: | Pranesh Santikellur, Rajat Subhra Chakraborty |
| Publisher: | Springer an imprint of Springer Nature Singapore |
| Format: | Hardback |
| Pagination: | 84 pages |
| Series: | Studies in Computational Intelligence |
| Genres: |
Electronics: circuits and components Expert systems / knowledge-based systems Artificial intelligence Mathematics Computer science |
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.
Deep Learning for Computational Problems in Hardware Security features in the following genres: Electronics: circuits and components, Expert systems / knowledge-based systems, Artificial intelligence, Mathematics, Computer science
Deep Learning for Computational Problems in Hardware Security is available in Hardback
Deep Learning for Computational Problems in Hardware Security was written by Pranesh Santikellur, Rajat Subhra Chakraborty and published by Springer an imprint of Springer Nature Singapore
Deep Learning for Computational Problems in Hardware Security has 84 pages
Yes it is part of Studies in Computational Intelligence series
£80.99