The ANN Based Modeling Attack and Security Enhancement of the Double-layer PUF
Xiaole Cui, Yongliang Chen, Wenqiang Ye, Xiaoxin Cui · 2021
The modeling attack is a serious threat to the physical unclonable function (PUF) circuits. The double-layer PUF was reported as a PUF scheme to resist the machine learning attacks, and its test chip was fabricated and tested. This work attacks the double-layer PUF successfully by an intentionally designed artificial neural network (ANN) model based on the working principle of the target PUF. To enhance the anti-modeling-attack capability of the double-layer PUF, the XORing and the dimensional extension techniques are proposed. The attack results show that the prediction accuracy of the proposed ANN-based model with the XORing and 3D extension techniques is as low as 50.09% in average. It manifests that the proposed security enhancement techniques are able to improve the resilience of the double-layer PUF against the modeling attacks effectively.