Deep Learning for Computational Problems in Hardware Security : Modeling Attacks on Strong Physically Unclonable Function Circuits

Pranesh Santikellur

ISBN 10: 9811940193 / ISBN 13: 9789811940194

Editore: Springer, Springer, 2023

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Druck auf Anfrage Neuware - Printed after ordering - 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.

Codice articolo 9789811940194

Dati bibliografici

• Titolo: Deep Learning for Computational Problems in Hardware Security : Modeling Attacks on Strong Physically Unclonable Function Circuits
• Editore: Springer, Springer
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 9811940193
• ISBN 13: 9789811940194
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 6 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 6 millimetri di profondità
• Peso: 166 grammi

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

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.