Deep Learning for Computational Problems in Hardware Security: Modeling Attacks on Strong Physically Unclonable Function Circuits - Rajat Subhra Chakraborty,Pranesh Santikellur
-20% with code BOOKS
Shipping in 12-18 days
30-day return policy
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, ... Full description
You May Also Like
TOP
If Anyone Builds It, Everyone Dies: The Case Against Superintelligent AI
Eliezer Yudkowsky, Nate Soares
-20% with code BOOKS
In stock at our supplier
TOP
The Design of Everyday Things
-20% with code BOOKS
In stock at our supplier
TOP
Open Circuits: The Inner Beauty of Electronic Components
Eric Schlaepfer, Windell Oskay
-20% with code BOOKS
In stock at our supplier
TOP
Get started with MicroPython on Raspberry Pi Pico: The Official Raspberry Pi Pico Guide
-20% with code BOOKS
In stock at our supplier
TOP
Nucleo Boards Programming with the STM32CubeIDE: Hands-on in more than 50 projects
-20% with code BOOKS
In stock at our supplier
TOP
Practical Electronics for Inventors
-20% with code BOOKS
In stock at our supplier
TOP
Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
Steven L. Brunton, J. Nathan Kutz
-20% with code BOOKS
In stock at our supplier
TOP
Fundamentals of Aerodynamics ISE
Christopher Cadou, John D. Anderson
-20% with code BOOKS
In stock at our supplier
TOP
Black Holes: The Key to Understanding the Universe
-20% with code BOOKS
In stock at our supplier
TOP
KiCad Like A Pro - Fundamentals and Projects: Getting started with the world's best open-source PCB tool
-20% with code BOOKS
In stock at our supplier
TOP
Foundations of Flavor: The Noma Guide to Fermentation: Including Step-By-Step Information on Making and Cooking with: Koji, Kombuchas, Shoyus, Misos, Vinegars, Garums, Lacto-Ferments and black Fruits Vegetables
-20% with code BOOKS
In stock at our supplier
TOP
Make: Electronics: Learning by Discovery: A hands-on primer for the new electronics enthusiast
-20% with code BOOKS
In stock at our supplier
TOP
The Idea Factory: Bell Labs and the Great Age of American Innovation
-20% with code BOOKS
In stock at our supplier
TOP
Flowers For Algernon: A Modern Literary Classic
-20% with code BOOKS
In stock at our supplier
TOP
Professional SMD Assembly: Practical Low-Cost Methods for Reliable PCB Production
-20% with code BOOKS
In stock at our supplier
TOP
Blood in the Machine: The Origins of the Rebellion Against Big Tech
-20% with code BOOKS
In stock at our supplier
TOP
Mastering the Arduino Uno R4: Programming and Projects for the Minima and WiFi
-20% with code BOOKS
In stock at our supplier
TOP
Learning the Art of Electronics: A Hands-On Lab Course
Thomas C. Hayes, Paul Horowitz, David Abrams
-20% with code BOOKS
In stock at our supplier
TOP
AI 2041: Ten Visions for Our Future
-20% with code BOOKS
In stock at our supplier
Description
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.
More Information
| Author | Rajat Subhra Chakraborty, Pranesh Santikellur |
|---|---|
| Publisher | Springer Nature Singapore |
| Release year | 2023 |
| Cover type | Softcover |
| EAN | 9789811940194 |