A Fourier Analysis Based Attack Against Physically Unclonable Functions

Ganji, Fatemeh; Tajik, Shahin; Seifert, Jean-Pierre

doi:10.1007/978-3-662-58387-6_17

Cited by 16 publications

(22 citation statements)

References 43 publications

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“…In particular, in [GTFS16], Ganji et al introduced a weak PAC learning algorithm, proved its equivalence to a strong PAC algorithm, and applied it to Bistable Ring PUFs. In [GTS18], Ganji et al enhanced their PAC learning methodology. They introduced an improper PAC learning algorithm, making use of Fourier analysis of PUFs in Boolean function representation.…”

Section: Related Workmentioning

confidence: 99%

Combining Optimization Objectives: New Modeling Attacks on Strong PUFs

Tobisch¹,

Aghaie²,

Becker³

2021

TCHES

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Strong Physical Unclonable Functions (PUFs), as a promising security primitive, are supposed to be a lightweight alternative to classical cryptography for purposes such as device authentication. Most of the proposed candidates, however, have been plagued by modeling attacks breaking their security claims. The Interpose PUF (iPUF), which has been introduced at CHES 2019, was explicitly designed with state-of-the-art modeling attacks in mind and is supposed to be impossible to break by classical and reliability attacks. In this paper, we analyze its vulnerability to reliability attacks. Despite the increased difficulty, these attacks are still feasible, against the original authors’ claim. We explain how adding constraints to the modeling objective streamlines reliability attacks and allows us to model all individual components of an iPUF successfully. In order to build a practical attack, we give several novel contributions. First, we demonstrate that reliability attacks can be performed not only with covariance matrix adaptation evolution strategy (CMA-ES) but also with gradient-based optimization. Second, we show that the switch to gradient-based reliability attacks makes it possible to combine reliability attacks, weight constraints, and Logistic Regression (LR) into a single optimization objective. This framework makes modeling attacks more efficient, as it exploits knowledge of responses and reliability information at the same time. Third, we show that a differentiable model of the iPUF exists and how it can be utilized in a combined reliability attack. We confirm that iPUFs are harder to break than regular XOR Arbiter PUFs. However, we are still able to break (1,10)-iPUF instances, which were originally assumed to be secure, with less than 107 PUF response queries.

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Section: Related Workmentioning

confidence: 99%

Combining Optimization Objectives: New Modeling Attacks on Strong PUFs

Tobisch¹,

Aghaie²,

Becker³

2021

TCHES

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show abstract

“…Besides these targeted attacks, an attack based on the Fourier spectrum properties of Boolean functions has been proposed which proves the vulnerability of multiple PUF classes [13]. In this work, the authors utilize the noise sensitivity property of Boolean functions to learn the Fourier coefficients of the Boolean function representing PUFs.…”

Section: Corollarymentioning

confidence: 99%

“…To the best of our knowledge, PUFmeter [7] is the maiden attempt in this direction, which provides robustness analysis of PUFs with respect to provable ML attacks. PUFmeter leverages average sensitivity, noise sensitivity and k-junta testing measures to come up with a robustness characterization of PUFs attributed by the LMN algorithm [24] based on Fourier analysis [13]. However, PUFmeter framework works with collected CRPs instead of PUF designs as input, hence lacks its adaptability to experiment over the PUF architectural varations and compositions.…”

mentioning

confidence: 99%

Puf-G

Chatterjee

Mukhopadhyay

Hazra

2020

Proceedings of the 39th International Conference on Computer-Aided Design

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“…To enable fair and consistent comparison in the PUF-related literature, we introduce random noise variations to the training sets to emulate the real practice of attacking XOR PUFs on deployed PUF devices when noisy conditions exist. The random noise variations introduced in our experiment can be conventionally modeled as random variables drawn from Gaussian distribution, as suggested by [1,9,23,29]. To do so, we add a random Gaussian noise G noise = gaussian (µ, σ) to the final delay difference ∆ shown in Equation (1).…”

Section: Noisy Attacking Modelmentioning

confidence: 99%

Noise-Resilient Neural Network-Based Adversarial Attack Modeling for XOR Physical Unclonable Functions

Aseeri

2020

JCSANDM

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Authentication plays an essential role in preventing unauthorized access to data and resources on the Internet of Things. Classical security mechanisms fall short of achieving security requirements against many physical attacks due to the resource-constraint nature of IoT devices. Physical Unclonable Functions (PUFs) have been successfully used for lightweight security applications such as devices authentication and secret key generation. PUFs utilize the inevitable variation of integrated circuits during the fabrication process to produce unique responses for individual PUF, hence not reproducible even by the manufacturer itself. However, PUFs are mathematically cloneable by machine learning-based methods. XOR arbiter PUFs are one group of PUFs that can withstand existing attack methods unless exceedingly long training time and large dataset size are applied. In this paper, large-sized XOR PUFs with 64-bit and 128-bit challenges were efficiently and effectively attacked using a carefully engineered neural network-based method. Our fine-tuned neural network-based adversarial models achieve 99% prediction accuracy on noise-free datasets and as low as 96% prediction accuracy on noisy datasets, using up to 55% smaller dataset size compared to existing works known to us. Revealing such vulnerabilities is essential for PUF developers to re-evaluate existing PUF designs, hence avoiding potential risks for IoT devices.

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A Fourier Analysis Based Attack Against Physically Unclonable Functions

Cited by 16 publications

References 43 publications

Combining Optimization Objectives: New Modeling Attacks on Strong PUFs

Combining Optimization Objectives: New Modeling Attacks on Strong PUFs

Puf-G

Noise-Resilient Neural Network-Based Adversarial Attack Modeling for XOR Physical Unclonable Functions

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