2019 IEEE International Symposium on Circuits and Systems (ISCAS) | 2019

PUFNet: A Deep Neural Network Based Modeling Attack for Physically Unclonable Function

 
 
 

Abstract


A deep neural network named PUFNet is proposed, which reveals the unreported threats to device authentication systems based on physically unclonable functions (PUFs). We demonstrates that, by employing novel techniques developed in deep learning community, such as ReLU activation function and Xavier initialization technique, PUFNet successfully predicts the responses of double-arbiter PUF (DAPUF) to unseen challenges with probability of 88.4%, which is 21.1% higher than the conventional method. Those results highlight an important fact that PUF-based authentication schemes should be carefully designed considering the rapid evolving machine learning technology.

Volume None
Pages 1-4
DOI 10.1109/ISCAS.2019.8702431
Language English
Journal 2019 IEEE International Symposium on Circuits and Systems (ISCAS)

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