Unintentional uncontrollable variations in the manufacturing process of integrated circuits are used to realize silicon primitives known as physical unclonable functions (PUFs). These primitives are used to create unique signatures for security purposes. Investigating the vulnerabilities of PUFs is of utmost importance to uphold their usefulness in secure applications. One such investigation includes exploring the susceptibility of PUFs to modeling attacks that aim at extracting the PUFs’ behavior. To date, these attacks have mainly focused on a single PUF instance where the targeted PUF is attacked using the model built based on the very same PUF’s challenge–response pairs or power side channel. In this article, we move one step forward and introduceCross-PUFattacks where a model is created using the power consumption of one PUF instance to attack another PUF created from the same GDSII file. Through SPICE simulations, we show that these attacks are highly effective in modeling PUF behaviors even in the presence of noise and mismatches in temperature and aging of the PUF used for modeling versus the targeted PUF. To mitigate theCross-PUFattacks, we then propose a lightweight countermeasure based on dual-rail and random initialization logic approaches called DRILL. We show that DRILL is highly effective in thwartingCross-PUFattacks.

To enhance the security of digital circuits, there is often a desire to dynamically generate, rather than statically store, random values used for identification and authentication purposes. Physically Unclonable Functions (PUFs) provide the means to realize this feature in an efficient and reliable way by utilizing commonly overlooked process variations that unintentionally occur during the manufacturing of integrated circuits (ICs) due to the imperfection of fabrication process. When given a challenge, PUFs produce a unique response. However, PUFs have been found to be vulnerable to modeling attacks where by using a set of collected challenge response pairs (CRPs) and training a machine learning model, the response can be predicted for unseen challenges. To combat this vulnerability, researchers have proposed techniques such as Challenge Obfuscation. However, as shown in this paper, this technique can be compromised via modeling the PUF's power side-channel. We first show the vulnerability of a state-of-the-art Challenge Obfuscated PUF (CO-PUF) against power analysis attacks by presenting our attack results on the targeted CO-PUF. Then we propose two countermeasures, as well as their hybrid version, that when applied to the CO-PUFs make them resilient against power side-channel based modeling attacks. We also provide some insights on the proper design metrics required to be taken when implementing these mitigations. Our simulation results show the high success of our attack in compromising the original Challenge Obfuscated PUFs (success rate > 98%) as well as the significant improvement on resilience of the obfuscated PUFs against power side-channel based modeling when equipped with our countermeasures.

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.

The silicon primitives known as Physically Unclonable Functions (PUFs) are used for various security purposes including key generation, device authentication, etc. Due to the imperfections in manufacturing process, PUFs produce their unique outputs (responses) for given input signals (challenges) fed to identical circuitry designs. Although PUFs are deployed to preserve security and are assumed to be unclonable, their functionality may still be compromised by modeling attacks. However, such attacks only target one single PUF aiming at reversing its behavior (based on a subset of its challenge-response pairs), and are not useful for attacking other PUFs. Moreover a subset of the target PUF's response has to be known by the attacker. This paper moves one step forward and investigates the possibility of Cross-PUF attacks in which a particular PUF's power fingerprints can be used to break another PUF's security. In these Cross-PUF attacks, the attacker has at his disposal a reference PUF, and uses its power side-channel to train a machine learning model which can be deployed to attack other identical PUFs. The experimental results show the high success of the proposed attacks even in presence of noise and temperature differences between the target PUF and the one used to train the model. We target arbiter-PUFs but we deduce that the findings extend to all its derivatives, e.g., XOR-PUFs and Feed-Forward-PUFs.