To address the security challenge of integrated circuits, the Physical Unclonable Function (PUF) is of great concern as the root of trust. However, the PUF circuits are suffering from the modeling attacks in recent years. The design of anti-modeling-attack PUF is still an open issue. The XbarPUF with both column swapping and XORing was reported as an anti-modeling-attack PUF in 2017. This work proposes a two-step attack method. The first step transforms the target PUF into the XbarPUFs with XORing only, based on the column swapping states. The second step attacks the XbarPUFs with XORing only by the intentionally designed Artificial Neural Network (ANN) model. This method can predict the challenge response pairs (CRPs) of the XbarPUF with both column swapping and XORing successfully. To enhance the anti-modeling-attack capability, an improved XbarPUF is further proposed, which uses the dynamic column swapping technique. The results show that the prediction accuracy of attacks for the XbarPUF with both column swapping and XORing and the proposed XbarPUF reaches 98.96% and 70%, respectively, if the training CRPs account for one millionth of the total CRPs. The proposed XbarPUF has a better anti-attack capability than the XbarPUF with both column swapping and XORing.

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.

The physical unclonable function (PUF) against the modeling attack is of great concern in recent years, since the modeling attack has been proved to be a serious security threat to the PUF circuits. The double-layer PUF was reported as a PUF scheme to resist the fully connected artificial neural network based modeling attack, and its test chip was fabricated and tested. This work proposes an artificial neural network (ANN) based modeling method according to the working principle of the target PUF, and successfully attacks the double-layer PUF. To enhance the anti-modeling-attack capability of the double-layer PUF, the address swapping, 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 proposed techniques drops obviously. And the prediction accuracy is about 50.04% if all the three proposed techniques are applied in combination. It manifests that the proposed security enhancement techniques are able to improve the resilience of the double-layer PUF against the modeling attacks effectively. Both the randomness and uniqueness of the improved doublelayer PUFs are approximate to the ideal value (50%), and the reliability of the improved PUFs remain unchanged compared with the original counterpart because the operations on the resistive random memory (RRAM) array are the same.