ID - 131905605 TI - Deep Learning for Computational Problems in Hardware Security : Modeling Attacks on Strong Physically Unclonable Function Circuits AU - Santikellur, Pranesh AU - Chakraborty, Rajat Subhra PY - 2023 SN - 9789811940170 9789811940163 9789811940187 9789811940194 PB - Singapore Springer Nature DB - UniCat KW - Mathematics KW - Electrical engineering KW - Computer science KW - Artificial intelligence. Robotics. Simulation. Graphics KW - Computer. Automation KW - computers KW - informatica KW - wiskunde KW - KI (kunstmatige intelligentie) KW - computerkunde KW - elektrische circuits KW - AI (artificiële intelligentie) UR - https://www.unicat.be/uniCat?func=search&query=sysid:131905605 AB - 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. ER -