Halak Machine Learning for Embedded System Security
1. Auflage 2022
ISBN: 978-3-030-94178-9
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
E-Book, Englisch, 160 Seiten
Reihe: Engineering (R0)
ISBN: 978-3-030-94178-9
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
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.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
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.




