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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.
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