東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

On the learnability of physically un...

Ganji, Fatemeh.

FindBook

Google Book

Amazon

博客來

On the learnability of physically unclonable functions

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	On the learnability of physically unclonable functions/ by Fatemeh Ganji.
作者:	Ganji, Fatemeh.
出版者:	Cham :Springer International Publishing : : 2018.,
面頁冊數:	xxiv, 86 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
Subject:	Machine learning. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-76717-8
ISBN:	9783319767178

On the learnability of physically unclonable functions
Ganji, Fatemeh.

On the learnability of physically unclonable functions[electronic resource] /by Fatemeh Ganji. - Cham :Springer International Publishing :2018. - xxiv, 86 p. :ill. (some col.), digital ;24 cm. - T-labs series in telecommunication services,2192-2810. - T-labs series in telecommunication services..

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.

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.

ISBN: 9783319767178

Standard No.: 10.1007/978-3-319-76717-8doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .G365 2018

Dewey Class. No.: 006.31

On the learnability of physically unclonable functions
LDR:03023nmm a2200325 a 4500 001 2136790
003 DE-He213
005 20180913102534.0
006 m d
007 cr nn 008maaau
008 181117s2018 gw s 0 eng d
020 $a 9783319767178 $q (electronic bk.)
020 $a 9783319767161 $q (paper)
024 7 $a 10.1007/978-3-319-76717-8 $2 doi
035 $a 978-3-319-76717-8
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .G365 2018
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .G197 2018
100 1 $a Ganji, Fatemeh. $3 3308544
245 1 0 $a On the learnability of physically unclonable functions $h [electronic resource] / $c by Fatemeh Ganji.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a xxiv, 86 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a T-labs series in telecommunication services, $x 2192-2810
505 0 $a 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.
520 $a 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.
650 0 $a Machine learning. $3 533906
650 0 $a Subroutines (Computer programs) $3 720560
650 0 $a Integrated circuits $x Security measures. $3 3308545
650 1 4 $a Engineering. $3 586835
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Coding and Information Theory. $3 891252
650 2 4 $a Mathematical Applications in Computer Science. $3 1567978
650 2 4 $a Circuits and Systems. $3 896527
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a T-labs series in telecommunication services. $3 2056823
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-76717-8
950 $a Engineering (Springer-11647)