Effect of Aging on PUF Modeling Attacks based on Power Side-Channel Observations

Thanks to the imperfections in manufacturing process, Physically Unclonable Functions (PUFs) produce their unique outputs for given input signals (challenges) fed to identical circuitry designs. PUFs are often used as hardware primitives to provide security, e.g., for key generation or authentication purposes. However, they can be vulnerable to modeling attacks that predict the output for an unknown challenge, based on a set of known challenge/response pairs (CRPs). In addition, an attacker may benefit from power side-channels to break a PUFs' security. Although such attacks have been extensively discussed in literature, the effect of device aging on the efficacy of these attacks is still an open question. Accordingly, in this paper, we focus on the impact of aging on Arbiter-PUFs and one of its modeling-resistant counterparts, the Voltage Transfer Characteristic (VTC) PUF. We present the results of our SPICE simulations used to perform modeling attack via Machine Learning (ML) schemes on the devices aged from 0 to 20 weeks. We show that aging has a significant impact on modeling attacks. Indeed, when the training dataset for ML attack is extracted at a different age than the evaluation dataset, the attack is greatly hindered despite being performed on the same device. We show that the ML attack via power traces is particularly efficient to recover the responses of the anti-modeling VTC PUF, yet aging still contributes to enhance its security.