Machine learning for embedded system security / edited by Basel Halak, editor.
2022
TK7895.E42
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Details
Title
Machine learning for embedded system security / edited by Basel Halak, editor.
ISBN
9783030941789 (electronic bk.)
3030941787 (electronic bk.)
9783030941772
3030941779
3030941787 (electronic bk.)
9783030941772
3030941779
Published
Cham : Springer, 2022.
Language
English
Description
1 online resource (1 volume) : illustrations (black and white, and colour).
Other Standard Identifiers
10.1007/978-3-030-94178-9 doi
Call Number
TK7895.E42
Dewey Decimal Classification
005.8
006.2/2
006.2/2
Summary
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. .
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Access limited to authorized users.
Source of Description
Description based on print version record.
Added Author
Halak, Basel, editor.
Available in Other Form
Machine learning for embedded system security.
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Table of Contents
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. .
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. .