Random pn-junctions for physical cryptography

Jaeger, Christian; Algasinger, Michael; Rührmair, Ulrich; Csaba, G.; Stutzmann, M.

doi:10.1063/1.3396186

Cited by 21 publications

(23 citation statements)

References 13 publications

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“…Also special PUFs with a very high information content (so-called SHIC PUFs [30,31,32]) could be an option, but only in such applications where their slow read-out speed and their comparatively large area consumption are no too strong drawbacks. Their promise is that they are naturally immune against modeling attacks, since all of their CRPs are informationtheoretically independent.…”

Section: Summary and Discussionmentioning

confidence: 99%

Modeling attacks on physical unclonable functions

Rührmair

Sehnke

Sölter

et al. 2010

Proceedings of the 17th ACM Conference on Computer and Communications Security

668

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We show in this paper how several proposed Physical Unclonable Functions (PUFs) can be broken by numerical modeling attacks. Given a set of challenge-response pairs (CRPs) of a PUF, our attacks construct a computer algorithm which behaves indistinguishably from the original PUF on almost all CRPs. This algorithm can subsequently impersonate the PUF, and can be cloned and distributed arbitrarily. This breaks the security of essentially all applications and protocols that are based on the respective PUF.The PUFs we attacked successfully include standard Arbiter PUFs and Ring Oscillator PUFs of arbitrary sizes, and XOR Arbiter PUFs, Lightweight Secure PUFs, and Feed-Forward Arbiter PUFs of up to a given size and complexity. Our attacks are based upon various machine learning techniques, including Logistic Regression and Evolution Strategies. Our work leads to new design requirements for secure electrical PUFs, and will be useful to PUF designers and attackers alike.

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Section: Summary and Discussionmentioning

confidence: 99%

Modeling attacks on physical unclonable functions

Rührmair

Sehnke

Sölter

et al. 2010

Proceedings of the 17th ACM Conference on Computer and Communications Security

668

View full text Add to dashboard Cite

show abstract

“…Also special PUFs with a very high information content (so-called SHIC PUFs [39], [40], [21]) could be an option, but only in such applications where their slow read-out speed and their comparatively large area consumption are no too strong drawbacks. Their promise is that they are naturally immune against modeling attacks, since all of their CRPs are informationtheoretically independent.…”

Section: B Discussionmentioning

confidence: 99%

PUF Modeling Attacks on Simulated and Silicon Data

Rührmair

Sölter

Sehnke

et al. 2013

IEEE Trans.Inform.Forensic Secur.

515

320

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Abstract-We discuss numerical modeling attacks on several proposed Strong Physical Unclonable Functions (PUFs). Given a set of challenge-response pairs (CRPs) of a Strong PUF, the goal of our attacks is to construct a computer algorithm which behaves indistinguishably from the original PUF on almost all CRPs. If successful, this algorithm can subsequently impersonate the Strong PUF, and can be cloned and distributed arbitrarily. It breaks the security of any applications that rest on the Strong PUF's unpredictability and physical unclonability. Our method is less relevant for other PUF types such as Weak PUFs; see Section I-B for a detailed discussion of this topic.The Strong PUFs that we could attack successfully include standard Arbiter PUFs of essentially arbitrary sizes, and XOR Arbiter PUFs, Lightweight Secure PUFs, and Feed-Forward Arbiter PUFs up to certain sizes and complexities. We also investigate the hardness of certain Ring Oscillator PUF architectures in typical Strong PUF applications. Our attacks are based upon various machine learning techniques, including a specially tailored variant of Logistic Regression and Evolution Strategies.Our results are mostly obtained on CRPs from numerical simulations that use established digital models of the respective PUFs. For a subset of the considered PUFs -namely standard Arbiter PUFs and XOR Arbiter PUFs -we also lead proofs of concept on silicon data from both FPGAs and ASICs. Over four million silicon CRPs are used in this process. The performance on silicon CRPs is very close to simulated CRPs, confirming a conjecture from earlier versions of this work. Our findings lead to new design requirements for secure electrical Strong PUFs, and will be useful to PUF designers and attackers alike.

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“…In order to separate and classify challenges into different robustness groups, we perform the following three experiments. We apply a 1000 sets of random challenges to the PUF while the tuning level is once set to 14, once 13 and once to 15 and we obtain 1000×16 responses for each case. Then the challenges that produce those responses that do not change across the three cases are marked as low robust challenges.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…The PUF produces binary responses based on the difference between the clock speed and some combinational circuit delay. Some instances of analog and digital PUFs that attempt to implement public cryptography are presented in [12][13][14][15].…”

Section: Related Workmentioning

confidence: 99%

FPGA PUF using programmable delay lines

Majzoobi

Koushanfar

Devadas

2010

2010 IEEE International Workshop on Information Forensics and Security

148

100

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Abstract-This paper proposes a novel approach for efficient implementation of a real-valued arbiter-based physical unclonable function (PUF) on FPGA. We introduce a high resolution programmable delay logic (PDL) implemented by lookup table (LUT) internal structure. Using the PDL, we perform fine tuning to cancel out delay skews caused by asymmetries in routing and systematic variations. We devise a symmetric switch structure that can be easily implemented on FPGA. To mitigate the arbiter metastability problem, we present and analyze methods for majority voting of responses. Lastly, a method to classify and group challenges into different robustness sets is introduced, to further increase the corresponding responses' stability in the face of environmental variations. Experimental evaluations show that the responses to robust challenges have an average error rate of less than 2% under temperature variations from -10 o C to 75 o C.

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Random pn-junctions for physical cryptography

Cited by 21 publications

References 13 publications

Modeling attacks on physical unclonable functions

Modeling attacks on physical unclonable functions

PUF Modeling Attacks on Simulated and Silicon Data

FPGA PUF using programmable delay lines

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