000			01670 a2200193 4500
005			20251114142429.0
008			250915b |||||||| |||| 00| 0 eng d
020			_a9789811940163
082			_a005.82 _bSanD
100			_a Pranesh Santikellur
245			_aDeep learning for computational problems in hardware security : _bmodeling attacks on strong physically unclonable function circuits / _c Pranesh Santikellur and Rajat Subhra Chakraborty.
260			_aSingapore: _bSpringer, _c©2021,
300			_axi, 84p.
440			_aStudies in Computational Intelligence _vv. 1052
520			_aThe 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.
650			_aMachine Learning _xBasics
700			_aChakraborty, Rajat Subhra
942			_cBK
999			_c7360 _d7360