PUFNet: A Deep Neural Network Based Modeling Attack for Physically Unclonable Function
Hiromitsu Awano, Tomoki Iizuka, Makoto Ikeda · 2019
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.