Research on security architecture of strong PUF by adversarial learning

Abstract

To overcome the vulnerability of strong physical unclonable function, the adversarial learning model of strong PUF was presented based on the adversarial learning theory, then the training process of gradient descent algorithm was analyzed under the framework of the model, the potential relationship between the delay vector weight and the prediction accuracy was clarified, and an adversarial sample generation strategy was designed based on the delay vector weight. Compared with traditional strategies, the prediction accuracy of logistic regression under new strategy was reduced by 5.4% ~ 9.5%, down to 51.4%. The physical structure with low overhead was designed corresponding to the new strategy, which then strengthened by symmetrical design and complex strategy to form a new PUF architecture called ALPUF. ALPUF not only decrease the prediction accuracy of machine learning to the level of random prediction, but also resist hybrid attack and brute force attack. Compared with other PUF security structures, ALPUF has advantages in overhead and security.

Keywords

• strong physical unclonable function
• adversarial sample
• delay vector
• adversarial learning puf