Deep learning for computational problems in hardware security : modeling attacks on strong physically unclonable function circuits / Pranesh Santikellur, Rajat Subhra Chakraborty.
- 作者： Santikellur, Pranesh, author.
- 其他作者：
- 其他題名：
- Modeling attacks on strong physically unclonable function circuits
- Studies in computational intelligence ;
- 出版： Singapore : Springer 2023.
- 叢書名： Studies in computational intelligence,volume 1052
- 主題： Deep learning (Machine learning). , Computer security.
- ISBN： 9789811940163 (hbk.): NT$3481 、 9811940169 (hbk.)
- 書目註：Includes bibliographical references
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讀者標籤：
- 系統號： 005179110 | 機讀編目格式
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摘要註
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.