MA-GRNN:a high-efficient modeling attack approach utilizing generalized regression neural network for XOR arbiter physical unclonable functions
Yanjiang Liu, Gaofeng Huang, Junwei Li, Pengfei Guo, Chunsheng Zhu, Zibin Dai
Abstract
In this paper, we propose a novel modeling attack approach to predict the responses of XOR arbiter physical unclonable functions (XOR APUFs), which improves the prediction accuracy and reduces the computational time. The high-dimensional mathematical model of XOR APUF is established and its weakness is analyzed. Furthermore, a modeling attack approach based on the generalized regression neural network (MA-GRNN) is introduced to approximate the responses of XOR APUFs. As a proof-of-concept, four popular machine learning algorithms are utilized to evaluate the attack efficacy of 3-XOR, 4-XOR, 5-XOR and 6-XOR APUF schemes. Experimental results show that the MA-GRNN achieves a high prediction accuracy compared to the other three modeling attack approaches while requiring less computational time simultaneously.