Deep Learning for Computational Problems in Hardware Security: Modeling Attacks on Strong Physically Unclonable Function Circuits
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Specifications
| Publisher | Springer |
| ISBN 13 | 9789811940163 |
| ISBN 10 | 9811940169 |
| Author | Pranesh Santikellur |
| Book Format | Hardcover |
| Language | English |
| Book Description | 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 |
| About the Author | 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. |
| Publication Date | 2022-09-16 |
| Number of Pages | 84 pages |
Deep Learning for Computational Problems in Hardware Security: Modeling Attacks on Strong Physically Unclonable Function Circuits
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