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Linear and non-linear machine learning attacks on physical unclonable functions. (arXiv:2301.02549v1 [cs.CR])
cs.CR updates on arXiv.org arxiv.org
In this thesis, several linear and non-linear machine learning attacks on
optical physical unclonable functions (PUFs) are presented. To this end, a
simulation of such a PUF is implemented to generate a variety of datasets that
differ in several factors in order to find the best simulation setup and to
study the behavior of the machine learning attacks under different
circumstances. All datasets are evaluated in terms of individual samples and
their correlations with each other. In the following, both …
attacks datasets end find functions machine machine learning non order physical puf simulation study terms under