Deep Learning for Computational Problems in Hardware Security: Modeling Attacks on Strong Physically Unclonable Function Circuits [Paperback]

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.Chapter 1: Introduction.- Chapter 2: Fundamental Concepts of Machine Learning.- Chapter 3: Supervised Machine Learning Algorithms for PUF Modeling Attacks.- Chapter 4: Deep Learning based PUF Modeling Attacks.- Chapter 5: Tensor Regression based PUF Modeling Attack.- Chapter 6: Binarized Neural Network based PUF Modeling.- Chapter 7: Conclusions and Future Work. 

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 seculc}