Deep learning for computational problems in hardware security : modeling attacks on strong physically unclonable function circuits / Pranesh Santikellur, Rajat Subhra Chakraborty.
2023
Q325.73
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Title
Deep learning for computational problems in hardware security : modeling attacks on strong physically unclonable function circuits / Pranesh Santikellur, Rajat Subhra Chakraborty.
ISBN
9789811940170 (electronic bk.)
9811940177 (electronic bk.)
9811940169
9789811940163
9811940177 (electronic bk.)
9811940169
9789811940163
Publication Details
Singapore : Springer, [2023]
Language
English
Description
1 online resource
Item Number
10.1007/978-981-19-4017-0 doi
Call Number
Q325.73
Dewey Decimal Classification
006.3/1
Summary
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.
Bibliography, etc. Note
Includes bibliographical references.
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Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed September 23, 2022).
Added Author
Chakraborty, Rajat Subhra, author.
Series
Studies in computational intelligence ; v. 1052. 1860-9503
Available in Other Form
Print version: 9789811940163
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Table of Contents
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. .
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. .