| 書目名稱 | On the Learnability of Physically Unclonable Functions | | 編輯 | Fatemeh Ganji | | 視頻video | http://file.papertrans.cn/702/701177/701177.mp4 | | 概述 | Addresses the issue of machine learning (ML) attacks on integrated circuits through physical unclonable functions (PUFs).Provides the mathematical proofs of the vulnerability of various PUF families.O | | 叢書名稱 | T-Labs Series in Telecommunication Services | | 圖書封面 |  | | 描述 | .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..?.Beside | | 出版日期 | Book 2018 | | 關(guān)鍵詞 | Physically Unclonable Function PUF; PUF vulnerability; ML attack; XOR Arbiter; ring-oscillator; bistable | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-319-76717-8 | | isbn_softcover | 978-3-030-09563-5 | | isbn_ebook | 978-3-319-76717-8Series ISSN 2192-2810 Series E-ISSN 2192-2829 | | issn_series | 2192-2810 | | copyright | Springer International Publishing AG, part of Springer Nature 2018 |
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