Machine Learning for Embedded System Security (Hardcover, 1st ed. 2022)

This book comprehensively covers the state-of-the-art security applications of machine learning techniques. The first part explains the emerging solutions for anti-tamper design, IC Counterfeits detection and hardware Trojan identification. It also explains the latest development of deep-learning-based modeling attacks on physically unclonable functions and outlines the design principles of more resilient PUF architectures. The second discusses the use of machine learning to mitigate the risks of security attacks on cyber-physical systems, with a particular focus on power plants. The third part provides an in-depth insight into the principles of malware analysis in embedded systems and describes how the usage of supervised learning techniques provides an effective approach to tackle software vulnerabilities.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Release date:	April 2022
First published:	2022
Editors:	Basel Halak
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	160
Edition:	1st ed. 2022
ISBN-13:	978-3-03-094177-2
Categories:	Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Circuits & components Books > Computing & IT > Computer hardware & operating systems > Computer architecture & logic design > General
LSN:	3-03-094177-9
Barcode:	9783030941772

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