  This paper contributes to the study of PUFs vulnerability against modeling
attacks by evaluating the security of XOR BR PUFs, XOR TBR PUFs, and obfuscated
architectures of XOR BR PUF using a simplified mathematical model and deep
learning (DL) techniques. Obtained results show that DL modeling attacks could
easily break the security of 4-input XOR BR PUFs and 4-input XOR TBR PUFs with
modeling accuracy $\sim$ 99%. Similar attacks were executed using single-layer
neural networks (NN) and support vector machines (SVM) with polynomial kernel
and the obtained results showed that single NNs failed to break the PUF
security. Furthermore, SVM results confirmed the same modeling accuracy
reported in previous research ($\sim$ 50%). For the first time, this research
empirically shows that DL networks can be used as powerful modeling techniques
against these complex PUF architectures for which previous conventional machine
learning techniques had failed. Furthermore, a detailed scalability analysis is
conducted on the DL networks with respect to PUFs' stage size and complexity.
The analysis shows that the number of layers and hidden neurons inside every
layer has a linear relationship with PUFs' stage size, which agrees with the
theoretical findings in deep learning. Consequently, A new obfuscated
architecture is introduced as a first step to counter DL modeling attacks and
it showed significant resistance against such attacks (16% - 40% less
accuracy). This research provides an important step towards prioritizing the
efforts to introduce new PUF architectures that are more secure and
invulnerable to modeling attacks. Moreover, it triggers future discussions on
the removal of influential bits and the level of obfuscation needed to confirm
that a specific PUF architecture is resistant against powerful DL modeling
attacks.
