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Deep learning for computational prob...

Santikellur, Pranesh.

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Deep learning for computational problems in hardware security = modeling attacks on strong physically unclonable function circuits /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep learning for computational problems in hardware security/ by Pranesh Santikellur, Rajat Subhra Chakraborty.
其他題名:	modeling attacks on strong physically unclonable function circuits /
作者:	Santikellur, Pranesh.
其他作者:	Chakraborty, Rajat Subhra.
出版者:	Singapore :Springer Nature Singapore : : 2023.,
面頁冊數:	xiii, 84 p. :ill., digital ;24 cm.
內容註:	Chapter 1: Introduction -- Chapter 2: Fundamental Concepts of Machine Learning -- Chapter 3: Supervised Machine Learning Algorithms for PUF Modeling Attacks -- Chapter 4: Deep Learning based PUF Modeling Attacks -- Chapter 5: Tensor Regression based PUF Modeling Attack -- Chapter 6: Binarized Neural Network based PUF Modeling -- Chapter 7: Conclusions and Future Work.
Contained By:	Springer Nature eBook
標題:	Deep learning (Machine learning) -
電子資源:	https://doi.org/10.1007/978-981-19-4017-0
ISBN:	9789811940170

Deep learning for computational problems in hardware security = modeling attacks on strong physically unclonable function circuits /
Santikellur, Pranesh.

Deep learning for computational problems in hardware securitymodeling attacks on strong physically unclonable function circuits /[electronic resource] :by Pranesh Santikellur, Rajat Subhra Chakraborty. - Singapore :Springer Nature Singapore :2023. - xiii, 84 p. :ill., digital ;24 cm. - Studies in computational intelligence,v. 10521860-9503 ;. - Studies in computational intelligence ;v. 1052..

Chapter 1: Introduction -- Chapter 2: Fundamental Concepts of Machine Learning -- Chapter 3: Supervised Machine Learning Algorithms for PUF Modeling Attacks -- Chapter 4: Deep Learning based PUF Modeling Attacks -- Chapter 5: Tensor Regression based PUF Modeling Attack -- Chapter 6: Binarized Neural Network based PUF Modeling -- Chapter 7: Conclusions and Future Work.

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: 9789811940170

Standard No.: 10.1007/978-981-19-4017-0doiSubjects--Topical Terms:

3538509
Deep learning (Machine learning)

LC Class. No.: Q325.73

Dewey Class. No.: 006.31

Deep learning for computational problems in hardware security = modeling attacks on strong physically unclonable function circuits /
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