Deep Learning for Computational Problems in Hardware Security : Modeling Attacks on Strong Physically Unclonable Function Circuits / Pranesh Santikellur, Rajat Subhra Chakraborty
Publication year: 2022
ISBN: 978-981-19-4017-0
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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.
Subject: Artificial Intelligence, Cybernetics, Deep learning, Hardware Security, Computer security, Machine learning, Machine Learning Algorithms, PUF Modeling Attack, Binarized Neural Network, Hardware Attacks, Hardware Security, Deep Neural Networks, Tensor Regression Networks, Physically Unclonable Function, Electronic Circuits and Systems, Mathematics in Popular Science, Special Purpose and Application-Based Systems
