عنوان

On the learnability of physically unclonable functions /

شابک

3030095630

شابک

3319767178

شابک

3319767186

شابک

9783030095635

شابک

9783319767178

شابک

9783319767185

شابک اشتباه

331976716X

شابک اشتباه

9783319767161

عنوان اصلي

On the learnability of physically unclonable functions /

نام عام مواد

[Book]

نام نخستين پديدآور

Fatemeh Ganji.

محل نشرو پخش و غیره

Cham, Switzerland :

نام ناشر، پخش کننده و غيره

Springer,

تاریخ نشرو بخش و غیره

2018.

نام خاص و کميت اثر

1 online resource (xxiv, 86 pages) :

ساير جزييات

illustrations (some color)

عنوان فروست

T-Labs series in telecommunication services,

شاپا ي ISSN فروست

2192-2810

متن يادداشت

Includes bibliographical references.

متن يادداشت

Introduction -- Definitions and Preliminaries -- PAC Learning of Arbiter PUFs -- PAC Learning of XOR Arbiter PUFs -- PAC Learning of Ring Oscillator PUFs -- PAC Learning of Bistable Ring PUFs -- Follow-up -- Conclusion.

بدون عنوان

0

متن يادداشت

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.

منبع سفارش / آدرس اشتراک

Springer Nature

شماره انبار

com.springer.onix.9783319767178

عنوان

On the learnability of physically unclonable functions.

شماره استاندارد بين المللي کتاب و موسيقي

9783319767161

موضوع مستند نشده

Integrated circuits-- Security measures.

موضوع مستند نشده

Machine learning.

موضوع مستند نشده

Subroutines (Computer programs)

موضوع مستند نشده

Artificial intelligence.

موضوع مستند نشده

Circuits & components.

موضوع مستند نشده

Coding theory & cryptology.

موضوع مستند نشده

COMPUTERS-- General.

موضوع مستند نشده

Machine learning.

موضوع مستند نشده

Mathematical modelling.

موضوع مستند نشده

Subroutines (Computer programs)

موضوع مستند نشده

COM-- 000000

موضوع مستند نشده

UYQ

موضوع مستند نشده

UYQ

شماره

ويراست

شماره رده

مستند نام اشخاص تاييد نشده

Ganji, Fatemeh

تاريخ عمليات

20200823111318.0

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

نوع ماده

[Book]

تكميل شده

Y