Statistical trend analysis of physically unclonable functions :, Zolfaghari, Behrouz,

Автор: Fatemeh Ganji
Название: On the Learnability of Physically Unclonable Functions
ISBN: 3030095630 ISBN-13(EAN): 9783030095635
Издательство: Springer
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Цена: 16769.00 р.
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Описание: 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.

Автор: Shahin Tajik
Название: On the Physical Security of Physically Unclonable Functions
ISBN: 3030093336 ISBN-13(EAN): 9783030093334
Издательство: Springer
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Цена: 13974.00 р.
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Описание: This book investigates the susceptibility of intrinsic physically unclonable function (PUF) implementations on reconfigurable hardware to optical semi-invasive attacks from the chip backside. It explores different classes of optical attacks, particularly photonic emission analysis, laser fault injection, and optical contactless probing. By applying these techniques, the book demonstrates that the secrets generated by a PUF can be predicted, manipulated or directly probed without affecting the behavior of the PUF. It subsequently discusses the cost and feasibility of launching such attacks against the very latest hardware technologies in a real scenario. The author discusses why PUFs are not tamper-evident in their current configuration, and therefore, PUFs alone cannot raise the security level of key storage. The author then reviews the potential and already implemented countermeasures, which can remedy PUFs’ security-related shortcomings and make them resistant to optical side-channel and optical fault attacks. Lastly, by making selected modifications to the functionality of an existing PUF architecture, the book presents a prototype tamper-evident sensor for detecting optical contactless probing attempts.

Автор: Ganji
Название: On the Learnability of Physically Unclonable Functions
ISBN: 331976716X ISBN-13(EAN): 9783319767161
Издательство: Springer
Рейтинг:
Цена: 13974.00 р.
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Описание:

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.

Автор: M?jgan Tez; Dietrich von Rosen
Название: Trends and Perspectives in Linear Statistical Inference
ISBN: 3319892428 ISBN-13(EAN): 9783319892429
Издательство: Springer
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Цена: 13974.00 р.
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Описание: This volume features selected contributions on a variety of topics related to linear statistical inference. The peer-reviewed papers from the International Conference on Trends and Perspectives in Linear Statistical Inference (LinStat 2016) held in Istanbul, Turkey, 22-25 August 2016, cover topics in both theoretical and applied statistics, such as linear models, high-dimensional statistics, computational statistics, the design of experiments, and multivariate analysis. The book is intended for statisticians, Ph.D. students, and professionals who are interested in statistical inference.

Автор: Tez
Название: Trends and Perspectives in Linear Statistical Inference
ISBN: 3319732404 ISBN-13(EAN): 9783319732404
Издательство: Springer
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Цена: 13974.00 р.
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Описание: Foreword.- Comparison of estimation methods for inverse Weibull distribution (F. G. Akgьl, B. Şenoğlu).- Liu-type negative binomial regression (Y. Asar).- Appraisal of performance of three tree-based classification methods (H. D. Asfha, B. K. Kilinc).- High-dimensional CLTs for individual Mahalanobis distances (D. Dai, T. Holgersson).- Bootstrap type-1 fuzzy functions approach for time series forecasting (A. Z. Dalar, E. Eğrioğlu).- A weighted ensemble learning by SVM for longitudinal data: Turkish bank bankruptcy (B. E. Erdogan, S. Ц. Akyьz).- The complementary exponential phase type distribution (S. Eryilmaz).- Best linear unbiased prediction: Some properties of linear prediction sufficiency in the linear model (J. Isotalo, A. Markiewicz, S. Puntanen).- A note on circular m-consecutive-k-out-of-n: F Systems (C. Kan).- A categorical principal component regression on computer assisted instruction in probability domain (T. Kapucu, O. Ilk, İ. Batmaz).- Contemporary robust optimal design strategies (T. E. O'Brien).- Alternative approaches for the use of uncertain prior information to overcome the rank-deficiency of a linear model (B. Schaffrin, K. Snow, X. Fang).- Exact likelihood-based point and interval estimation for lifetime characteristics of Laplace distribution based on hybrid Type-I and Type-II censored data (F. Su, N. Balakrishnan, X. Zhu).- Statistical inference for two-compartment model parameters with bootstrap method and genetic algorithm (Ц. Tьrkşen, M. Tez).