TRGP: A Low-Cost Re-Configurable TRNG-PUF Architecture for IoT

Kumar, Vikash; Tripathy, Somanath; Mathew, Jimson

doi:10.1109/isqed51717.2021.9424347

Cited by 5 publications

(4 citation statements)

References 25 publications

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“…In [18] the author continued the work in [19] and demonstrated N-bit read and write memristive PUF verifying its efficiency through measuring the uniqueness, uniformity, and bit-aliasing to measure the statistical quality of the M-PUF. A TRNG-based memristor PUF has been proposed in [21] providing a lightweight cryptographic primitive of high-quality entropy and small area without any post-processing algorithms or circuits. However, no further statistical tests of modeling attacks have been studied.…”

Section: Related Work and Contributionmentioning

confidence: 99%

Resilience Evaluation of Memristor-based PUF Against Machine Learning Attacks

Ibrahim,

Skovorodnikov,

Alkhzaimi

2024

Preprint

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Physical unclonable functions (PUFs) have emerged as a favorable hardware security primitive, they exploit the process variations to provide unique signatures. CMOS-based PUFs are the most popular type, however, most existing CMOS PUFs are found to be vulnerable to modeling attacks. Memristors leveraging nanotechnology fabrication processes and highly nonlinear behavior became an interesting alternative to the existing CMOS-based PUF technology. Memristor-based PUFs are emerging due to the inherent randomness at both the memristor level due to the cycle-to-cycle (C2C) programming variation of the device. Our study focuses on building a machine learning (ML) analysis and attack framework of tools on Cu/HfO2-x/p++Si memristor-based PUF (MR-PUF). Our objective is to test the resiliency of the security margins of the presented PUF using ML analysis tools. Our main contribution is a holistic study that focuses on attacking the randomness output resiliency based on building randomness predictors using Logistic Regression (LR), Support Vector Machine (SVM), Gaussian Mixture Models (GMM), K-means, K-means++, Random Forest, XGBoost and LSTM, within efficient time, and data complexity. Our results yield low accuracy and ROC results of within 0.49-0.52 and 0.49-0.52 respectively, indicating failure in predicting random data demonstrates efficient randomness prediction resiliency of the MR-PUF. The efficient time and data complexities of these attacks illustrated in this study are yielded to be linear and quadratic resulting in attack execution time in seconds and 5032 training samples combined with 2157 testing samples to verify the randomness of PUF.

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Section: Related Work and Contributionmentioning

confidence: 99%

Resilience Evaluation of Memristor-based PUF Against Machine Learning Attacks

Ibrahim,

Skovorodnikov,

Alkhzaimi

2024

Preprint

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“…Consequently, this design needs a long time to test every single cell in the entropy source array and a large memory to store the addresses of the cells with different entropy sources. Rai [19] proposed a memristor-based TRNG and PUF unified design. Memristor devices are prone to manufacturing defects that have known to cause unique faulty behaviors [20].…”

Section: Introductionmentioning

confidence: 99%

“…One main motivation and common application cited for these TRNG and PUF unified designs is lightweight authentication. However, the use of a weak PUF in a unified design, such as [16]- [19], [21], does not provide enough fresh challenges to meet the number of access requests for most authentication applications. Strong PUFs (SPUFs) are more suited to device authentication but they are vulnerable to modeling attacks.…”

Section: Introductionmentioning

confidence: 99%

A New Reconfigurable True Random Number Generator and Physical Unclonable Function Unified Chip With On-Chip Auto-Calibration

Cao,

Liu,

Zheng

et al. 2023

IEEE Trans. Circuits Syst. I

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“…Periodically alternating between these states allows the degree of randomness to be increased. Some authors have also analyzed the possibility of obtaining random bit sequences through certain types of RO-based physical unclonable functions (PUFs) [28][29][30]. PUFs have become fundamental elements in increasing the security level of systems, as they are used to authenticate electronic devices and generate cryptographic keys, taking advantage of the physical variations that occur naturally during semiconductor manufacturing [31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

True Random Number Generation Capability of a Ring Oscillator PUF for Reconfigurable Devices

et al. 2022

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This paper presents the validation of a novel approach for a true-random number generator (TRNG) based on a ring oscillator–physical unclonable function (RO-PUF) for FPGA devices. The proposal takes advantage of the different noise sources that affect the electronic implementation of the RO-PUF to extract the entropy required to guarantee its function as a TRNG, without anything more than minimal changes to the original design. The new RO-PUF/TRNG architecture has been incorporated within a hybrid HW/SW embedded system designed for devices from the Xilinx Zynq-7000 family. The degree of randomness of the generated bit streams was assessed using the NIST 800-22 statistical test suite, while the validation of the RO-PUF proposal as an entropy source was carried out by fulfilling the NIST 800-90b recommendation. The features of the hybrid system were exploited to carry out the evaluation and validation processes proposed by the NIST publications, online and on the same platform. To establish the optimal configuration to generate bit streams with the appropriate entropy level, a statistical study of the degree of randomness was performed for multiple TRNG approaches derived from the different implementation modes and configuration options available on the original RO-PUF design. The results show that the RO-PUF/TRNG design is suitable for secure cryptographic applications, doubling its functionality without compromising the resource–efficiency trade-off already achieved in the design.

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TRGP: A Low-Cost Re-Configurable TRNG-PUF Architecture for IoT

Cited by 5 publications

References 25 publications

Resilience Evaluation of Memristor-based PUF Against Machine Learning Attacks

Resilience Evaluation of Memristor-based PUF Against Machine Learning Attacks

A New Reconfigurable True Random Number Generator and Physical Unclonable Function Unified Chip With On-Chip Auto-Calibration

True Random Number Generation Capability of a Ring Oscillator PUF for Reconfigurable Devices

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