عنوان

On the learnability of physically unclonable functions /

(Number (ISBN

3030095630

(Number (ISBN

3319767178

(Number (ISBN

3319767186

(Number (ISBN

9783030095635

(Number (ISBN

9783319767178

(Number (ISBN

9783319767185

Erroneous ISBN

331976716X

Erroneous ISBN

9783319767161

Title Proper

On the learnability of physically unclonable functions /

General Material Designation

[Book]

First Statement of Responsibility

Fatemeh Ganji.

Place of Publication, Distribution, etc.

Cham, Switzerland :

Name of Publisher, Distributor, etc.

Springer,

Date of Publication, Distribution, etc.

2018.

Specific Material Designation and Extent of Item

1 online resource (xxiv, 86 pages) :

Other Physical Details

illustrations (some color)

Series Title

T-Labs series in telecommunication services,

ISSN of Series

2192-2810

Text of Note

Includes bibliographical references.

Text of Note

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.

0

Text of Note

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.

Source for Acquisition/Subscription Address

Springer Nature

Stock Number

com.springer.onix.9783319767178

Title

On the learnability of physically unclonable functions.

International Standard Book Number

9783319767161

Integrated circuits-- Security measures.

Machine learning.

Subroutines (Computer programs)

Artificial intelligence.

Circuits & components.

Coding theory & cryptology.

COMPUTERS-- General.

Machine learning.

Mathematical modelling.

Subroutines (Computer programs)

COM-- 000000

UYQ

UYQ

Number

Edition

Class number

Ganji, Fatemeh

Date of Transaction

20200823111318.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y