Sprache: Englisch
Verlag: Springer, Berlin|Springer International Publishing|Springer, 2023
ISBN 10: 3030941809 ISBN 13: 9783030941802
Anbieter: moluna, Greven, Deutschland
EUR 92,27
Anzahl: Mehr als 20 verfügbar
In den WarenkorbKartoniert / Broschiert. Zustand: New.
Sprache: Englisch
Verlag: Springer, Berlin|Springer International Publishing|Springer, 2023
ISBN 10: 3030941779 ISBN 13: 9783030941772
Anbieter: moluna, Greven, Deutschland
EUR 92,27
Anzahl: Mehr als 20 verfügbar
In den WarenkorbGebunden. Zustand: New.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 150,67
Anzahl: 2 verfügbar
In den WarenkorbPaperback. Zustand: Brand New. 175 pages. 9.25x6.10x0.38 inches. In Stock.
Zustand: New.
Sprache: Englisch
Verlag: Springer International Publishing, 2023
ISBN 10: 3030941809 ISBN 13: 9783030941802
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
Sprache: Englisch
Verlag: Springer International Publishing, 2022
ISBN 10: 3030941779 ISBN 13: 9783030941772
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.