Modelling Attack Analysis of Configurable Ring Oscillator (CRO) PUF Designs

Miskelly, Jack; Gu, Chongyan; Qing, Qing; Cui, Yijun; Liu, Weiqiang; OrNeill, Maire

doi:10.1109/icdsp.2018.8631638

Cited by 18 publications

(5 citation statements)

References 26 publications

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“…The total delay difference ∆D is the superposition of ∆D (i). While ∆D (i) for traditional CRO PUFs (∆D (i) of CRO PUF [7], [8], RRO PUF [10], XCRO PUF [9] can be considered the same) is proved to be ∆D [16]. According to Eq.…”

Section: A Delay Model Of Transformer Pufmentioning

confidence: 98%

“…As analysed above, conventional CRO PUFs are vulnerable to modeling attack since their delay model are relatively simple. Previous MPUF attempted to increase the resistance to machine learning attack by XORing the challenges with the unknown outputs of weak PUFs in [16]. However, the improved MPUF still suffers from the machine learning attack while consuming more hardware resources.…”

Section: B Machine Learning Attacks On Transformer Pufmentioning

confidence: 99%

“…PUFs are vulnerable to modeling attacks [16]. To have a comprehensive security analysis of the proposed Transformer PUF, the mathematical delay model is built and analysed.…”

mentioning

confidence: 99%

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Transformer PUF : A Highly Flexible Configurable RO PUF Based on FPGA

Wei

Cui

Chen

et al. 2020

2020 IEEE Workshop on Signal Processing Systems (SiPS)

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Physical unclonable function (PUF) is a promising security primitive for IP protection and user authentication. As there are plenty of reconfigurable resources in a field programmable gate array (FPGA), configurable ring oscillator (CRO) PUF is one of the most hardware efficient PUF designs. Previous CRO PUF designs have relatively improved the yield of challenge and response pairs (CRPs). In this paper, a highly flexible CRO PUF based on FPGA, defined as Transformer PUF, is proposed. The proposed PUF design, which has multiple reconfigurability from XOR gates and multiplexers, can be deformable between different CRO PUFs. Compared with the traditional CRO PUFs, it is more resistant to two common machine learning attack techniques, logistic regression (LR) and covariance matrix adaptation evolutionary strategies (CMA-ES), with a small sample set size. Moreover, the Transformer PUF achieves the highest hardware efficiency among CRO PUFs. The results of the experiment carried out on Xilinx Artix-7 development board demonstrate that Transformer PUF has a good uniqueness of 49.44% and a high reliability of 98.12%.

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Section: A Delay Model Of Transformer Pufmentioning

confidence: 98%

Section: B Machine Learning Attacks On Transformer Pufmentioning

confidence: 99%

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Transformer PUF : A Highly Flexible Configurable RO PUF Based on FPGA

Wei

Cui

Chen

et al. 2020

2020 IEEE Workshop on Signal Processing Systems (SiPS)

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“…CRO-PUF Miskelly et al [89] Main advantage of CRO over RO is that it is much more efficient in terms of space and component usage.…”

Section: Sklavos Et Al [87]mentioning

confidence: 99%

FPGA-Based PUF Designs: A Comprehensive Review and Comparative Analysis

Lata,

Cenkeramaddi

2023

Cryptography

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Field-programmable gate arrays (FPGAs) have firmly established themselves as dynamic platforms for the implementation of physical unclonable functions (PUFs). Their intrinsic reconfigurability and profound implications for enhancing hardware security make them an invaluable asset in this realm. This groundbreaking study not only dives deep into the universe of FPGA-based PUF designs but also offers a comprehensive overview coupled with a discerning comparative analysis. PUFs are the bedrock of device authentication and key generation and the fortification of secure cryptographic protocols. Unleashing the potential of FPGA technology expands the horizons of PUF integration across diverse hardware systems. We set out to understand the fundamental ideas behind PUF and how crucially important it is to current security paradigms. Different FPGA-based PUF solutions, including static, dynamic, and hybrid systems, are closely examined. Each design paradigm is painstakingly examined to reveal its special qualities, functional nuances, and weaknesses. We closely assess a variety of performance metrics, including those related to distinctiveness, reliability, and resilience against hostile threats. We compare various FPGA-based PUF systems against one another to expose their unique advantages and disadvantages. This study provides system designers and security professionals with the crucial information they need to choose the best PUF design for their particular applications. Our paper provides a comprehensive view of the functionality, security capabilities, and prospective applications of FPGA-based PUF systems. The depth of knowledge gained from this research advances the field of hardware security, enabling security practitioners, researchers, and designers to make wise decisions when deciding on and implementing FPGA-based PUF solutions.

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“…Several works perform a successful modeling attack on the arbiterbased PUFs [2], RO-based PUFs [3] and its improvements [4]. Furthermore, some MA-resilience PUFs, including the XOR arbiter PUF [5], bistable ring PUF [6], twisted bistable ring PUF [7], double arbiter PUF [8], configurable ring oscillator PUF [9], and lightweight secure PUF [10], are not immune to advanced modeling attacks. On the one hand, modeling attacks with machine learning algorithms have become a serious threat to the security of PUFs and PUFbased solutions.…”

Section: Introductionmentioning

confidence: 99%

MA-GRNN:a high-efficient modeling attack approach utilizing generalized regression neural network for XOR arbiter physical unclonable functions

Liu

Huang

et al. 2023

IEICE Electron. Express

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In this paper, we propose a novel modeling attack approach to predict the responses of XOR arbiter physical unclonable functions (XOR APUFs), which improves the prediction accuracy and reduces the computational time. The high-dimensional mathematical model of XOR APUF is established and its weakness is analyzed. Furthermore, a modeling attack approach based on the generalized regression neural network (MA-GRNN) is introduced to approximate the responses of XOR APUFs. As a proof-of-concept, four popular machine learning algorithms are utilized to evaluate the attack efficacy of 3-XOR, 4-XOR, 5-XOR and 6-XOR APUF schemes. Experimental results show that the MA-GRNN achieves a high prediction accuracy compared to the other three modeling attack approaches while requiring less computational time simultaneously.

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Modelling Attack Analysis of Configurable Ring Oscillator (CRO) PUF Designs

Cited by 18 publications

References 26 publications

Transformer PUF : A Highly Flexible Configurable RO PUF Based on FPGA

Transformer PUF : A Highly Flexible Configurable RO PUF Based on FPGA

FPGA-Based PUF Designs: A Comprehensive Review and Comparative Analysis

MA-GRNN:a high-efficient modeling attack approach utilizing generalized regression neural network for XOR arbiter physical unclonable functions

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