A Hybrid-Optimizer-Enhanced Neural Network Method for the Security Vulnerability Study of Multiplexer Arbiter PUFs

Abstract

With the advent of the Internet of Things, security has become indispensable. Physical unclonable functions (PUFs) are emerging as a promising alternative to classical cryptographic algorithms as it provides a lightweight and cost-effective solution for implementing a keyless security mechanism. Before adopting a PUF for real-world applications, a thorough examination of all important properties of PUF is necessary, and security and reliability are two of the important properties. The multiplexer based PUF (MPUF) was recently designed to improve upon the reliability while maintaining a similar resistance to machine learning (ML) attacks as compared with XOR PUFs of certain sizes. Recently, feed-forward neural network (NN) methods were found to be an effective tool for studying PUFs’ security against ML attacks, and a study in 2019 found that some MPUFs are insecure against NN attack methods. In this paper, we try to gain further insight into various factors of NNs that affect the predictive power of NN as PUF attack methods. We investigate a NN that employs different optimizers at different stages of the machine learning process, leading to what is called a hybrid-optimizer-enhanced NN. We implemented the new NN for ML attack of MPUFs, and experimental results have shown it converges faster than a traditional NN with a single optimizer on attacking MPUFs, and the new method also requires less training data as compared with a recent NN-based attack study of MPUFs.