A Survey on the Susceptibility of PUFs to Invasive, Semi-Invasive and Noninvasive Attacks: Challenges and Opportunities for Future Directions

Mispan, Mohd Syafiq; Halak, Basel; Zwoliński, Mark

doi:10.1142/s0218126621300099

Cited by 6 publications

(4 citation statements)

References 69 publications

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“…For example, it might include the 'physical' susceptibility of a PUF, meaning how well CRPs can be detected using a side channel attack. 40,43 Or, it might represent how many CRPs are required to train an ML-based attack that can predict responses better than chance. 16-23, 25, 26, 33, 34, 42 Because a primary rationale for the photonic PUF is its lesser vulnerability to physical CRP-collection attacks, we assess susceptibility as the number of CRPs required to launch an ML-based attack that can predict responses for as-ofyet-unseen challenges with a probability greater than chance.…”

Section: Brief Introduction To Physically Unclonable Functionsmentioning

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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Section: Brief Introduction To Physically Unclonable Functionsmentioning

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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“…In particular, resource-constrained IoT devices are more likely to be suffered from full and direct access [1], [2]. Attackers can directly access critical data and even digital keys stored in the chip through hardware attack techniques such as physical probing, invasive, and semiinvasive [3]. Therefore, traditional security techniques are no longer sufficient to ensure the security of IoT systems, and more secure and efficient protection measures are required.…”

Section: Introductionmentioning

confidence: 99%

Reliable and Efficient Chip-PCB Hybrid PUF and Lightweight Key Generator

XU¹,

KE²,

Fu³

et al. 2023

IEICE Trans. Electron.

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Physical Unclonable Function (PUF) is a promising lightweight hardware security primitive that can extract device fingerprints for encryption or authentication. However, extracting fingerprints from either the chip or the board individually has security flaws and cannot provide hardware system-level security. This paper proposes a new Chip-PCB hybrid PUF(CPR PUF) in which Weak PUF on PCB is combined with Strong PUF inside the chip to generate massive responses under the control of challenges of on-chip Strong PUF. This structure tightly couples the chip and PCB into an inseparable and unclonable unit thus can verify the authenticity of chip as well as the board. To improve the uniformity and reliability of Chip-PCB hybrid PUF, we propose a lightweight key generator based on a reliability self-test and debiasing algorithm to extract massive stable and secure keys from unreliable and biased PUF responses, which eliminates expensive error correction processes. The FPGA-based test results show that the PUF responses after robust extraction and debiasing achieve high uniqueness, reliability, uniformity and anti-counterfeiting features. Moreover, the key generator greatly reduces the execution cost and the bit error rate of the keys is less than 10 −9 , the overall security of the key is also improved by eliminating the entropy leakage of helper data.

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“…Several PUFs have been proposed in the past [2], [3]. Static random-access memory PUF (SRAM-PUF) is one of the previously proposed PUFs and it is categorized as memory-based PUF [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Lightweight hardware fingerprinting solution using inherent memory in off-the-shelf commodity devices

Mispan

Jidin

Kamarudin

et al. 2022

IJEECS

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An emerging technology known as Physical unclonable function (PUF) can provide a hardware root-of-trust in building the trusted computing system. PUF exploits the intrinsic process variations during the integrated circuit (IC) fabrication to generate a unique response. This unique response differs from one PUF to the other similar type of PUFs. Static random-access memory PUF (SRAM-PUF) is one of the memory-based PUFs in which the response is generated during the memory power-up process. Non-volatile memory (NVM) architecture like SRAM is available in off-the-shelf microcontroller devices. Exploiting the inherent SRAM as PUF could wide-spread the adoption of PUF. Therefore, in this study, we evaluate the suitability of inherent SRAM available in ATMega2560 microcontroller on Arduino platform as PUF that can provide a unique fingerprint. First, we analyze the start-up values (SUVs) of memory cells and select only the cells that show random values after the power-up process. Subsequently, we statistically analyze the characteristic of fifteen SRAM-PUFs which include uniqueness, reliability, and uniformity. Based on our findings, the SUVs of fifteen on-chip SRAMs achieve 42.64% uniqueness, 97.28% reliability, and 69.16% uniformity. Therefore, we concluded that the available SRAM in off-the-shelf commodity hardware has good quality to be used as PUF.

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A Survey on the Susceptibility of PUFs to Invasive, Semi-Invasive and Noninvasive Attacks: Challenges and Opportunities for Future Directions

Cited by 6 publications

References 69 publications

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

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

Reliable and Efficient Chip-PCB Hybrid PUF and Lightweight Key Generator

Lightweight hardware fingerprinting solution using inherent memory in off-the-shelf commodity devices

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