Skip to main content
SpringerLink
Log in

Deep Learning for Computational Problems in Hardware Security

Modeling Attacks on Strong Physically Unclonable Function Circuits

  • Book
  • © 2023

Overview

Authors:
  1. Pranesh Santikellur

    1. Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India

    View author publications

    You can also search for this author in PubMed   Google Scholar

  2. Rajat Subhra Chakraborty

    1. Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • Discusses the various challenges present in the hardware security domain and how deep learning can solve it better
  • Introduces different deep learning-based techniques to solve several important hardware security problems
  • Describes machine learning methods and state-of-the-art deep learning practices for hardware security applications

Part of the book series: Studies in Computational Intelligence (SCI, volume 1052)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (7 chapters)

  1. Front Matter

    Pages i-xiii
    Download chapter PDF

  2. Introduction to Machine Learning for Hardware Security

    • Pranesh Santikellur, Rajat Subhra Chakraborty
    Pages 1-7

  3. Physically Unclonable Functions

    • Pranesh Santikellur, Rajat Subhra Chakraborty
    Pages 9-22

  4. Machine-Learning Basics

    • Pranesh Santikellur, Rajat Subhra Chakraborty
    Pages 23-34

  5. Modeling Attacks on PUF

    • Pranesh Santikellur, Rajat Subhra Chakraborty
    Pages 35-53

  6. Improved Modeling Attack on PUFs based on Tensor Regression Network

    • Pranesh Santikellur, Rajat Subhra Chakraborty
    Pages 55-69

  7. Combinational Logic-Based Implementation of PUF

    • Pranesh Santikellur, Rajat Subhra Chakraborty
    Pages 71-82

  8. Conclusion

    • Pranesh Santikellur, Rajat Subhra Chakraborty
    Pages 83-84
Back to top

Keywords

About this book

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.

Authors and Affiliations

  • Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India

    Pranesh Santikellur, Rajat Subhra Chakraborty

About the authors

Pranesh Santikellur is a Ph.D. student and a Senior Research Fellow in the Department of Computer Science and Engineering at the Indian Institute of Technology, Kharagpur. He received his B.E. degree in Electronics & Communication Engineering from Visvesvaraya Technological University, Belgaum, India, in 2010. He has a total of 6 years of industry experience at Horner Engineering India Pvt. Ltd. and Processor Systems. His primary research interest lies in hardware security, deep learning, and programmable logic controller security. He is an IEEE student member.

Rajat Subhra Chakraborty is an Associate Professor in the Department of Computer Science & Engineering of the Indian Institute of Technology, Kharagpur, India. He has professional experience working in National Semiconductor and Advanced Micro Devices (AMD). His research interest lies in the areas of hardware security, VLSI design, digital watermarking, and digital image forensics, in which he has published 4 books and over 100 papers in international journals and conferences of repute. He holds 2 granted U.S. patents. His publications have received over 3600 citations to date. Dr. Chakraborty has a Ph.D. in Computer Engineering from Case Western Reserve University, USA, and is a senior member of IEEE and ACM.

Bibliographic Information

  • Book Title: Deep Learning for Computational Problems in Hardware Security

  • Book Subtitle: Modeling Attacks on Strong Physically Unclonable Function Circuits

  • Authors: Pranesh Santikellur, Rajat Subhra Chakraborty

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-981-19-4017-0

  • Publisher: Springer Singapore

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023

  • Hardcover ISBN: 978-981-19-4016-3Published: 16 September 2022

  • Softcover ISBN: 978-981-19-4019-4Published: 17 September 2023

  • eBook ISBN: 978-981-19-4017-0Published: 15 September 2022

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XIII, 84

  • Number of Illustrations: 13 b/w illustrations, 18 illustrations in colour

  • Topics: Circuits and Systems, Artificial Intelligence, Mathematics, general, Special Purpose and Application-Based Systems, Computer Science, general

Publish with us

Policies and ethics

Back to top

Search

Navigation