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Machine learning for embedded system...

Halak, Basel.

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Machine learning for embedded system security

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning for embedded system security/ edited by Basel Halak.
other author:	Halak, Basel.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	xv, 160 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Machine Learning for Tamper Detection -- Machine Learning for IC Counterfeit Detection and Prevention -- Machine Learning for Secure PUF Design -- Machine Learning for Malware Analysis -- Machine Learning for Detection of Software Attacks -- Conclusions and Future Opportunities.
Contained By:	Springer Nature eBook
Subject:	Embedded computer systems - Security measures. -
Online resource:	https://doi.org/10.1007/978-3-030-94178-9
ISBN:	9783030941789

Machine learning for embedded system security
Machine learning for embedded system security[electronic resource] /edited by Basel Halak. - Cham :Springer International Publishing :2022. - xv, 160 p. :ill., digital ;24 cm.

Introduction -- Machine Learning for Tamper Detection -- Machine Learning for IC Counterfeit Detection and Prevention -- Machine Learning for Secure PUF Design -- Machine Learning for Malware Analysis -- Machine Learning for Detection of Software Attacks -- Conclusions and Future Opportunities.

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. Discusses emerging technologies used to develop intelligent tamper detection techniques, using machine learning; Includes a comprehensive summary of how machine learning is used to combat IC counterfeit and to detect Trojans; Describes how machine learning algorithms are used to enhance the security of physically unclonable functions (PUFs); It describes, in detail, the principles of the state-of-the-art countermeasures for hardware, software, and cyber-physical attacks on embedded systems.

ISBN: 9783030941789

Standard No.: 10.1007/978-3-030-94178-9doiSubjects--Topical Terms:

1085966
Embedded computer systems
--Security measures.

LC Class. No.: TK7895.E42 / M33 2022

Dewey Class. No.: 005.8

Machine learning for embedded system security
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245 0 0 $a Machine learning for embedded system security $h [electronic resource] / $c edited by Basel Halak.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xv, 160 p. : $b ill., digital ; $c 24 cm.
505 0 $a Introduction -- Machine Learning for Tamper Detection -- Machine Learning for IC Counterfeit Detection and Prevention -- Machine Learning for Secure PUF Design -- Machine Learning for Malware Analysis -- Machine Learning for Detection of Software Attacks -- Conclusions and Future Opportunities.
520 $a 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. Discusses emerging technologies used to develop intelligent tamper detection techniques, using machine learning; Includes a comprehensive summary of how machine learning is used to combat IC counterfeit and to detect Trojans; Describes how machine learning algorithms are used to enhance the security of physically unclonable functions (PUFs); It describes, in detail, the principles of the state-of-the-art countermeasures for hardware, software, and cyber-physical attacks on embedded systems.
650 0 $a Embedded computer systems $x Security measures. $3 1085966
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650 2 4 $a Electronics Design and Verification. $3 3592716
650 2 4 $a Processor Architectures. $3 892680
700 1 $a Halak, Basel. $3 3321500
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856 4 0 $u https://doi.org/10.1007/978-3-030-94178-9
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