  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 linear and deep
learning approaches are used to attack these PUF simulations and
comprehensively investigate the impact of different factors on the datasets in
terms of their security level against attackers. In addition, the differences
between the two attack methods in terms of their performance are highlighted
using several independent metrics. Several improvements to these models and new
attacks will be introduced and investigated sequentially, with the goal of
progressively improving modeling performance. This will lead to the development
of an attack capable of almost perfectly predicting the outputs of the
simulated PUF. In addition, data from a real optical PUF is examined and both
compared to that of the simulation and used to see how the machine learning
models presented would perform in the real world. The results show that all
models meet the defined criterion for a successful machine learning attack.
