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Machine Learning for Embedded System Security

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

23.04.2023

Abbildungen

XV, 160 p. 66 illus., 39 illus. in color.

Herausgeber

Basel Halak

Verlag

Springer

Seitenzahl

160

Maße (L/B/H)

23,5/15,5/1 cm

Gewicht

277 g

Auflage

23001 Auflage 1st ed. 2022

Sprache

Englisch

ISBN

978-3-030-94180-2

Beschreibung

Portrait


Dr. Basel Halak is the director of the embedded systems and IoT program at the University of Southampton, a visiting scholar at the Technical University of Kaiserslautern, a visiting professor at the Kazakh-British Technical University, an industrial fellow of the royal academy of engineering, and a national teaching fellow of the Advance Higher Education(HE) Academy. Dr. Halak's publications include over 80-refereed conference and journal papers and authored four books, including the first textbook on Physically Unclonable Functions. His research expertise includes evaluation of the security of hardware devices, development of countermeasures, mathematical formalism of reliability issues in CMOS circuits (e.g. crosstalk, radiation, aging), and the use of fault tolerance techniques to improve the robustness of electronics systems against such issues. Dr. Halak lectures on digital design, Secure Hardware, and Cryptography.  Dr. Halak serves on several technical program committees such as HOST, IEEE DATE, IVSW, and DAC. He is an associate editor of IEEE access and an editor of the IET circuit devices and system journal. He is also a member of the hardware security-working group of the World Wide Web Consortium (W3C).

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

23.04.2023

Abbildungen

XV, 160 p. 66 illus., 39 illus. in color.

Herausgeber

Basel Halak

Verlag

Springer

Seitenzahl

160

Maße (L/B/H)

23,5/15,5/1 cm

Gewicht

277 g

Auflage

23001 Auflage 1st ed. 2022

Sprache

Englisch

ISBN

978-3-030-94180-2

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Machine Learning for Embedded System Security
  • Produktbild: Machine Learning for Embedded System Security

  • 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.