Titlebook: On the Learnability of Physically Unclonable Functions; Fatemeh Ganji Book 2018 Springer International Publishing AG, part of Springer Nat

Orthodontics

PAC Learning of XOR Arbiter PUFs,er, besides the development of a PAC learning framework for XOR Arbiter PUFs, a theoretical limit for ML attacks as a function of the number of the chains and the number of Arbiter PUF stages has been established. Furthermore, we show that our approach deals with the noisy responses in an efficient

遺忘

PAC Learning of Bistable Ring PUFs, first published in Ganji et al. (International Conference on Cryptographic Hardware and Embedded Systems–CHES 2016, Springer, Berlin, pp. 391–411, 2016, [32], J Cryptogr Eng, 2017, [33]). Some sections of this paper have been slightly reworked to be included in this thesis.

增減字母法

Follow-Up Work,The complete description of these work and their results have been first published in Tajik et al., 2015 Workshop on Fault Diagnosis and Tolerance in Cryptography (FDTC). pp. 85–96, 2015, ([110]), Ganji et al., Proceedings of the 22nd ACM Conference on Computer and communications security. ACM, 2015

重疊

Conclusion and Future Work,t also remedy the shortcomings of the corresponding traditional countermeasures. However, after more than a decade of the invention of PUFs, the design of a PUF fulfilling the necessary, and natural requirements is still a challenging task. Being unpredictable and unclonable are involved in these re

Harness

Book 2018athematical 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 inhe

WAX

Follow-Up Work,Cryptography (FDTC). pp. 85–96, 2015, ([110]), Ganji et al., Proceedings of the 22nd ACM Conference on Computer and communications security. ACM, 2015 ([31]). Here we merely focus on the key goals of these studies and methodologies applied to achieve them.

含沙射影

云狀

Flatus

PAC Learning of Arbiter PUFs,37–249, 2010, [95]), where it is not clear whether after the learning phase, a model of the Arbiter PUF with the desired level of accuracy would be delivered by the machine learner. Finally, we discuss the importance of our framework from the practical point of view.

CUMB

PAC Learning of XOR Arbiter PUFs, matches the results of experiments, which can be found in the literature. Last but not least, on the basis of learning theory concepts, this chapter explicitly states that the current form of XOR Arbiter PUFs may not be considered as an ultimate solution to the problem of insecure Arbiter PUFs.