000			03377nam a22003375i 4500
001			102955
003			DE-He213
005			20230102113107.0
007			cr nn 008mamaa
008			180324s2018 gw | s |||| 0|eng d
020			_a9783319767178
024	7		_a10.1007/978-3-319-76717-8 _2doi
040			_aES-MaUEC _bspa
050		4	_aQ325.5 2018 EB
100	1		_aGanji, Fatemeh _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aOn the Learnability of Physically Unclonable Functions _cby Fatemeh Ganji.
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XXIV, 86 páginas 21 ilustraciones, 4 ilustraciones a color)
347			_atext file _bPDF
490	0		_aT-Labs Series in Telecommunication Services _x2192-2810
505	0		_aIntroduction -- 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	3		_aThis 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		7	_aAprendizaje automático _2embne _9166090
650		7	_aCodificación, Teoría de la _2embne _9666753
776	0	8	_iEdición impresa: _z9783319767161
776	0	8	_iEdición impresa: _z9783319767185
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-76717-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
490	0		_aEngineering (Springer-11647)
988			_aEBSPRINGER_2018
998			_b02/2019 _dz _ek _feng _ggw _h0
999			_c102955 _d102955 _x1