Why Attackers Win: On the Learnability of XOR Arbiter PUFs

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

doi:10.1007/978-3-319-22846-4_2

Cited by 71 publications

(47 citation statements)

References 22 publications

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“…Recent theoretical machine learning results from [11] based on Valient's PAC theory concluded certain XOR PUF constructions to be learnable in polynomial time given a polynomial number of CRPs; other constructions were declared non-PAC-learnable and it was also suggested that these were also infeasible in practice due to PUF noise.…”

Section: Machine Learning Complexity Theory Resultsmentioning

confidence: 99%

“…• We are the first to confirm the existence of an exponentially hard-to-learn PUF system-level instantiation that is practically realizable, with the hardness based on PAC learning theory [11] (cf. Sec.…”

Section: Contributionmentioning

confidence: 88%

“…We also present practically realizable PUF instantiations that cannot be learned with resources polynomial with respect to the circuit size based on recent PUF learning results [11] using probably-approximately-correct (PAC) theory [12]. Previously, non-PAC-learnable instantiations were thought to be infeasible.…”

Section: Contributionmentioning

confidence: 99%

“…Sec. 9.3) that were missing in [11] make this result possible, and we include silicon validation (cf. Sec 7.1, Sec.…”

Section: Contributionmentioning

confidence: 99%

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A Lockdown Technique to Prevent Machine Learning on PUFs for Lightweight Authentication

Hiller

Delvaux

et al. 2016

IEEE Trans. Multi-Scale Comp. Syst.

176

150

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Abstract. We present a lightweight PUF-based authentication approach that is practical in settings where a server authenticates a device, and for use cases where the number of authentications is limited over a device's lifetime. Our scheme uses a server-managed challenge/response pair (CRP) lockdown protocol: unlike prior approaches, an adaptive chosenchallenge adversary with machine learning capabilities cannot obtain new CRPs without the server's implicit permission. The adversary is faced with the problem of deriving a PUF model with a limited amount of machine learning training data. Our system-level approach allows a so-called strong PUF to be used for lightweight authentication in a manner that is heuristically secure against today's best machine learning methods through a worst-case CRP exposure algorithmic validation. We also present a degenerate instantiation using a weak PUF that is secure against computationally unrestricted adversaries, which includes any learning adversary, for practical device lifetimes and read-out rates. We validate our approach using silicon PUF data, and demonstrate the feasibility of supporting 10, 1000, and 1M authentications, including practical configurations that are not learnable with polynomial resources, e.g., the number of CRPs and the attack runtime, using recent results based on the probably-approximately-correct (PAC) complexity-theoretic framework.

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Section: Machine Learning Complexity Theory Resultsmentioning

confidence: 99%

Section: Contributionmentioning

confidence: 88%

Section: Contributionmentioning

confidence: 99%

“…Sec. 9.3) that were missing in [11] make this result possible, and we include silicon validation (cf. Sec 7.1, Sec.…”

Section: Contributionmentioning

confidence: 99%

See 2 more Smart Citations

A Lockdown Technique to Prevent Machine Learning on PUFs for Lightweight Authentication

Hiller

Delvaux

et al. 2016

IEEE Trans. Multi-Scale Comp. Syst.

176

150

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“…7. Similarly, an attack published by Ganji et al [13] has been proven to run in polynomial time, but only under the precondition that there is only a constant number of parallel Arbiter PUFs.…”

Section: Related Workmentioning

confidence: 99%

Why Attackers Lose: Design and Security Analysis of Arbitrarily Large XOR Arbiter PUFs

Wisiol

Graebnitz²,

Margraf

et al.

EPiC Series in Computing

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In a novel analysis, we formally prove that arbitrarily many Arbiter PUFs can be combined into a stable XOR Arbiter PUF. To the best of our knowledge, this design cannot be modeled by any known oracle access attack in polynomial time.Using majority vote of arbiter chain responses, our analysis shows that with a polynomial number of votes, the XOR Arbiter PUF stability of almost all challenges can be boosted exponentially close to 1; that is, the stability gain through majority voting can exceed the stability loss introduced by large XORs for a feasible number of votes. Considering state-of-the-art modeling attacks by Becker and Rührmair et al., our proposal enables the designer to increase the attacker's effort exponentially while still maintaining polynomial design effort. This is the first result that relates PUF design to this traditional cryptographic design principle.

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Hardware-Based Authentication Applications

Arafin

2021

Authentication of Embedded Devices

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Why Attackers Win: On the Learnability of XOR Arbiter PUFs

Cited by 71 publications

References 22 publications

A Lockdown Technique to Prevent Machine Learning on PUFs for Lightweight Authentication

A Lockdown Technique to Prevent Machine Learning on PUFs for Lightweight Authentication

Why Attackers Lose: Design and Security Analysis of Arbitrarily Large XOR Arbiter PUFs

Hardware-Based Authentication Applications

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