Von dem Buch On the Learnability of Physically Unclonable Functions haben wir 3 gleiche oder sehr ähnliche Ausgaben identifiziert!
Falls Sie nur an einem bestimmten Exempar interessiert sind, können Sie aus der folgenden Liste jenes wählen, an dem Sie interessiert sind:
100%: Fatemeh Ganji: On the Learnability of Physically Unclonable Functions (ISBN: 9783319767178) 2018, in Englisch, Taschenbuch.Nur diese Ausgabe anzeigen…
100%: Fatemeh Ganji: On the Learnability of Physically Unclonable Functions (T-Labs Series in Telecommunication Services) (ISBN: 9783319767161) 2018, Erstausgabe, in Englisch, Broschiert.Nur diese Ausgabe anzeigen…
100%: Fatemeh Ganji: On the Learnability of Physically Unclonable Functions (ISBN: 9783030095635) Springer Shop, in Deutsch, Taschenbuch.Nur diese Ausgabe anzeigen…
On the Learnability of Physically Unclonable Functions
9 Angebote vergleichen
Bester Preis: Fr. 4.91 (€ 5.02)¹ (vom 06.09.2019)1
On the Learnability of Physically Unclonable Functions
~EN NW EB DLISBN: 9783319767178 bzw. 3319767178, vermutlich in Englisch, Springer Shop, neu, E-Book, elektronischer Download.
Lieferung aus: Österreich, Lagernd, zzgl. Versandkosten.
This book addresses the issue of Machine Learning (ML) attacks on Integrated Circuits through Physical Unclonable Functions (PUFs). It provides the mathematical proofs of the vulnerability of various PUF families, including Arbiter, XOR Arbiter, ring-oscillator, and bistable ring PUFs, to ML attacks. To achieve this goal, it develops a generic framework for the assessment of these PUFs based on two main approaches. First, with regard to the inherent physical characteristics, it establishes fit-for-purpose mathematical representations of the PUFs mentioned above, which adequately reflect the physical behavior of these primitives. To this end, notions and formalizations that are already familiar to the ML theory world are reintroduced in order to give a better understanding of why, how, and to what extent ML attacks against PUFs can be feasible in practice. Second, the book explores polynomial time ML algorithms, which can learn the PUFs under the appropriate representation. More importantly, in contrast to previous ML approaches, the framework presented here ensures not only the accuracy of the model mimicking the behavior of the PUF, but also the delivery of such a model. Besides off-the-shelf ML algorithms, the book applies a set of algorithms hailing from the field of property testing, which can help to evaluate the security of PUFs. They serve as a “toolbox”, from which PUF designers and manufacturers can choose the indicators most relevant for their requirements. Last but not least, on the basis of learning theory concepts, the book explicitly states that the PUF families cannot be considered as an ultimate solution to the problem of insecure ICs. As such, it provides essential insights into both academic research on and the design and manufacturing of PUFs. eBook.
This book addresses the issue of Machine Learning (ML) attacks on Integrated Circuits through Physical Unclonable Functions (PUFs). It provides the mathematical proofs of the vulnerability of various PUF families, including Arbiter, XOR Arbiter, ring-oscillator, and bistable ring PUFs, to ML attacks. To achieve this goal, it develops a generic framework for the assessment of these PUFs based on two main approaches. First, with regard to the inherent physical characteristics, it establishes fit-for-purpose mathematical representations of the PUFs mentioned above, which adequately reflect the physical behavior of these primitives. To this end, notions and formalizations that are already familiar to the ML theory world are reintroduced in order to give a better understanding of why, how, and to what extent ML attacks against PUFs can be feasible in practice. Second, the book explores polynomial time ML algorithms, which can learn the PUFs under the appropriate representation. More importantly, in contrast to previous ML approaches, the framework presented here ensures not only the accuracy of the model mimicking the behavior of the PUF, but also the delivery of such a model. Besides off-the-shelf ML algorithms, the book applies a set of algorithms hailing from the field of property testing, which can help to evaluate the security of PUFs. They serve as a “toolbox”, from which PUF designers and manufacturers can choose the indicators most relevant for their requirements. Last but not least, on the basis of learning theory concepts, the book explicitly states that the PUF families cannot be considered as an ultimate solution to the problem of insecure ICs. As such, it provides essential insights into both academic research on and the design and manufacturing of PUFs. eBook.
2
On the Learnability of Physically Unclonable Functions
~EN NW EB DLISBN: 9783319767178 bzw. 3319767178, vermutlich in Englisch, Springer International Publishing, neu, E-Book, elektronischer Download.
Lieferung aus: Deutschland, Versandkostenfrei.
On the Learnability of Physically Unclonable Functions: This book addresses the issue of Machine Learning (ML) attacks on Integrated Circuits through Physical Unclonable Functions (PUFs). It provides the mathematical proofs of the vulnerability of various PUF families, including Arbiter, XOR Arbiter, ring-oscillator, and bistable ring PUFs, to ML attacks. To achieve this goal, it develops a generic framework for the assessment of these PUFs based on two main approaches. First, with regard to the inherent physical characteristics, it establishes fit-for-purpose mathematical representations of the PUFs mentioned above, which adequately reflect the physical behavior of these primitives. To this end, notions and formalizations that are already familiar to the ML theory world are reintroduced in order to give a better understanding of why, how, and to what extent ML attacks against PUFs can be feasible in practice. Second, the book explores polynomial time ML algorithms, which can learn the PUFs under the appropriate representation. More importantly, in contrast to previous ML approaches, the framework presented here ensures not only the accuracy of the model mimicking the behavior of the PUF, but also the delivery of such a model. Englisch, Ebook.
On the Learnability of Physically Unclonable Functions: This book addresses the issue of Machine Learning (ML) attacks on Integrated Circuits through Physical Unclonable Functions (PUFs). It provides the mathematical proofs of the vulnerability of various PUF families, including Arbiter, XOR Arbiter, ring-oscillator, and bistable ring PUFs, to ML attacks. To achieve this goal, it develops a generic framework for the assessment of these PUFs based on two main approaches. First, with regard to the inherent physical characteristics, it establishes fit-for-purpose mathematical representations of the PUFs mentioned above, which adequately reflect the physical behavior of these primitives. To this end, notions and formalizations that are already familiar to the ML theory world are reintroduced in order to give a better understanding of why, how, and to what extent ML attacks against PUFs can be feasible in practice. Second, the book explores polynomial time ML algorithms, which can learn the PUFs under the appropriate representation. More importantly, in contrast to previous ML approaches, the framework presented here ensures not only the accuracy of the model mimicking the behavior of the PUF, but also the delivery of such a model. Englisch, Ebook.
3
On the Learnability of Physically Unclonable Functions (2018)
EN NW EB DLISBN: 9783319767178 bzw. 3319767178, in Englisch, Springer, Springer, Springer, neu, E-Book, elektronischer Download.
Lieferung aus: Vereinigtes Königreich Grossbritannien und Nordirland, in-stock.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
5
On the Learnability of Physically Unclonable Functions
~EN PB NWISBN: 9783319767178 bzw. 3319767178, vermutlich in Englisch, Springer-Verlag GmbH, Taschenbuch, neu.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
6
On the Learnability of Physically Unclonable Functions
DE PB NWISBN: 9783030095635 bzw. 3030095630, in Deutsch, Springer Shop, Taschenbuch, neu.
Lieferung aus: Mexiko, Lagernd, zzgl. Versandkosten.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
7
Symbolbild
On the Learnability of Physically Unclonable Functions (T-Labs Series in Telecommunication Services) (2018)
EN HC NW FEISBN: 9783319767161 bzw. 331976716X, in Englisch, Springer, gebundenes Buch, neu, Erstausgabe.
Lieferung aus: Deutschland, Noch nicht erschienen. Versandkostenfrei.
Von Händler/Antiquariat, Amazon.de.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Von Händler/Antiquariat, Amazon.de.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
8
On the Learnability of Physically Unclonable Functions
DE NW EBISBN: 9783319767178 bzw. 3319767178, in Deutsch, Springer Nature, neu, E-Book.
Lieferung aus: Vereinigtes Königreich Grossbritannien und Nordirland, Lagernd.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
9
On the Learnability of Physically Unclonable Functions
DE HC NWISBN: 9783319767161 bzw. 331976716X, in Deutsch, Springer International Publishing, gebundenes Buch, neu.
Lieferung aus: Vereinigte Staaten von Amerika, Lagernd, zzgl. Versandkosten.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Lade…