Improving Machine Learning Attack Resiliency via Conductance Balancing in Memristive Strong PUFs

Previous works have shown excellent prospects for implementing strong physical unclonable functions (PUFs) with memristive crossbar circuits. Here we first propose two techniques for boosting the robustness of such PUFs to machine learning (ML) attacks. The general idea behind both proposals is to maximize the contribution of each crosspoint device to the PUF output to make the response less predictable. Specifically, we present results for choosing an optimal ratio of selected rows and columns, and investigate in detail the improvements in robustness due to the balancing of device conductances in the crossbar array. The effectiveness of the proposed algorithm for conductance balancing is confirmed by modeling the response of a two-sided PUF based on a 20
$\times20$
crossbar memristive circuit with a multilayer perceptron network. Second, we explore some open questions which require in-depth analysis. Specifically, we quantify the effect of device nonlinearity and device analog-tunability. We show that nonlinear, analog memristive PUFs outperform the PUFs that have either linear or digital devices. Finally, we explore the effect of stuck-at-fault devices (nonideal yield) on PUFs uniformity. Indeed, by modeling this hardware imperfection, we show that the proposed algorithm results in a more-robust PUF.