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Machine Learning for Embedded System Security [electronic resource] / edited by Basel Halak.

Contributor(s): Material type: TextTextLanguage: İngilizce Publisher: Cham : Springer International Publishing : Imprint: Springer, 2022Edition: 1st ed. 2022Description: 1 online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783030941789
Subject(s): LOC classification:
  • TK7895.E42
Online resources:
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. .
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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Holdings
Item type Current library Home library Collection Call number Copy number Status Notes Date due Barcode
E-Book E-Book Merkez Kütüphane Merkez Kütüphane E-Kitap Koleksiyonu TK7895.E42EBK (Browse shelf(Opens below)) 1 Geçerli değil-e-Kitap / Not applicable-e-Book EBK03232

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. .

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Devinim Yazılım Eğitim Danışmanlık tarafından Koha'nın orjinal sürümü uyarlanarak geliştirilip kurulmuştur.