Efficient Attack on Non-linear Current Mirror PUF with Genetic Algorithm

Guo, Qingli; Ye, Jing; Gong, Yue; Hu, Yuxiao; Li, Xiaowei

doi:10.1109/ats.2016.21

Cited by 22 publications

(5 citation statements)

References 4 publications

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“…For PUFs, [70,73] are two of the first studies focusing on this by employing evolution strategies and genetic programming against feed-forward Arbiter PUFs and RO PUFs, respectively. In this context, the security of recently emerging PUF architectures, e.g., non-linear current mirror PUFs [47] has been compromised through genetic algorithms [34].…”

Section: Ai-enabled Attacksmentioning

confidence: 99%

Physically Unclonable Functions and AI: Two Decades of Marriage

Ganji¹,

Tajik²

2020

Preprint

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Section: Ai-enabled Attacksmentioning

confidence: 99%

Physically Unclonable Functions and AI: Two Decades of Marriage

Ganji¹,

Tajik²

2020

Preprint

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“…1,[11][12][13][14][15] Machine learning (ML)-based attacks that predict responses for as-of-yet-unseen challenges have become an increasing threat to PUFs of all variants. 5,[16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] These attacks do not directly represent the transformation between challenges and responses, but rather predict the result of that transformation after learning from a set of CRPs collected from a given PUF. Machine-learning-based attacks, including regression mappings, state vector machines, k-nearest neighbors, random forests, genetic algorithms, artificial neural networks, and generative adversarial networks, have proven effective against various conventional PUFs, and additional work has developed theoretical relationships between the number of CRPs used for training and response prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Designing a photonic physically unclonable function having resilience to machine learning attacks

Henderson,

Shahoei

et al. 2024

Quantum Information Science, Sensing, and Computation XVI

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Physically unclonable functions (PUFs) are designed to act as device 'fingerprints.' Given an input challenge, the PUF circuit should produce an unpredictable response for use in situations such as root-of-trust applications and other hardware-level cybersecurity applications. PUFs are typically subcircuits present within integrated circuits (ICs), and while conventional IC PUFs are well-understood, several implementations have proven vulnerable to malicious exploits, including those perpetrated by machine learning (ML)-based attacks. Such attacks can be difficult to prevent because they are often designed to work even when relatively few challenge-response pairs are known in advance. Hence the need for both more resilient PUF designs and analysis of ML-attack susceptibility. Previous work has developed a PUF for photonic integrated circuits (PICs). A PIC PUF not only produces unpredictable responses given manufacturing-introduced tolerances, but is also less prone to electromagnetic radiation eavesdropping attacks than a purely electronic IC PUF. In this work, we analyze the resilience of the proposed photonic PUF when subjected to ML-based attacks. Specifically, we describe a computational PUF model for producing the large datasets required for training ML attacks; we analyze the quality of the model; and we discuss the modeled PUF's susceptibility to ML-based attacks. We find that the modeled PUF generates distributions that resemble uniform white noise, explaining the exhibited resilience to neural-network-based attacks designed to exploit latent relationships between challenges and responses. Preliminary analysis suggests that the PUF exhibits similar resilience to generative adversarial networks, and continued development will show whether more-sophisticated ML approaches better compromise the PUF and-if so-how design modifications might improve resilience.

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“…However, they cannot meet the needs of IoT devices due to the limited density and scaling trend of CMOS technology; therefore, CMOS-based PUF designs encounter the same constraints, such as high power dissipation and large area. It is well known that most PUF designs are vulnerable to machine learning (ML) attacks [6]; therefore, new anti-attack PUF designs are needed. When the feature size is reduced to nanoscales, the design and manufacturing of ICs face even greater challenges.…”

Section: Introductionmentioning

confidence: 99%

A Physical Unclonable Function Using a Configurable Tristate Hybrid Scheme With Non-Volatile Memory

Cui

et al. 2021

IEEE Open J. Nanotechnol.

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The physical unclonable function (PUF) is a promising low-cost hardware security primitive. Recent advances in nanotechnology have provided new opportunities for nanoscale PUF circuits. The resistive random access memory (RRAM) is extensively used in nanoscale circuits due to its low cost, non-volatility and easy integration with CMOS. This paper proposes a novel tristate hybrid PUF (TH-PUF) design based on a one-transistor-one-RRAM (1T1R) cell; this cell can be configured into two weak PUFs and a strong PUF using few control signals. To assess the proposed PUF design, a compact RRAM model at UMC 65nm technology is employed. Simulation results show that the proposed TH-PUF achieves good uniqueness, reliability as well as a higher gate usability compared with an entire CMOS PUFs. The number of challenge response pairs (CRPs) of the proposed TH-PUF is larger than other RRAM-based PUFs. Moreover, the TH-PUF is more resistant to a modeling machine learning attack than traditional PUF designs.

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Efficient Attack on Non-linear Current Mirror PUF with Genetic Algorithm

Cited by 22 publications

References 4 publications

Physically Unclonable Functions and AI: Two Decades of Marriage

Physically Unclonable Functions and AI: Two Decades of Marriage

Designing a photonic physically unclonable function having resilience to machine learning attacks

A Physical Unclonable Function Using a Configurable Tristate Hybrid Scheme With Non-Volatile Memory

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